import numpy as np
import pandas as pd
from math import pi,exp
import matplotlib.pyplot as plt
%matplotlib inline
Read the training data from file ex1-data-train.csv. The first two columns are x1 and x2. The last column holds the class label y. Compose suitable numpy array structures.
trainset = pd.read_csv('scores_train_2.csv',names=['x1','x2','y'])
testset = pd.read_csv('scores_test_2.csv',names=['x1','x2','y'])
testset.head()
| x1 | x2 | y | |
|---|---|---|---|
| 0 | 39.196334 | 78.530294 | 0 |
| 1 | 40.448499 | 86.839470 | 1 |
| 2 | 65.571920 | 44.303497 | 0 |
| 3 | 79.648113 | 70.806564 | 1 |
| 4 | 66.260221 | 41.672703 | 0 |
x1 = np.array(trainset['x1'].values)
x2 = np.array(trainset['x2'].values)
m = x1.size
x_train0 = np.concatenate([x1,x2],axis=0).reshape(2,m)
y_train = np.array(trainset['y'].values).reshape(1,m)
x1 = np.array(testset['x1'].values)
x2 = np.array(testset['x2'].values)
x_test0 = np.concatenate([x1,x2],axis=0).reshape(2,m)
y_test = np.array(testset['y'].values).reshape(1,m)
print("Training Set: ", x_train0.shape, y_train.shape)
print("Test Set: ", x_test0.shape, y_test.shape)
Training Set: (2, 100) (1, 100) Test Set: (2, 100) (1, 100)
Plot the training data using a scatter plot.
def plot_dataset(x, y):
n = x.shape[1]
x1 = x[0,:].reshape(1,n)
x2 = x[1,:].reshape(1,n)
plot_data(x1,x2,y)
def plot_data(x1,x2,y):
indices_pass = np.where(y==1)
indices_fail = np.where(y==0)
x1_pass = x1[indices_pass]
x2_pass = x2[indices_pass]
x1_fail = x1[indices_fail]
x2_fail = x2[indices_fail]
plt.scatter(x1_fail,x2_fail,marker='o',color='green',label='not admitted')
plt.scatter(x1_pass,x2_pass,marker='x',color='red',label='admitted')
plt.xlabel('Exam 1 score $x_1$')
plt.ylabel('Exam 2 score $x_2$')
plt.legend(bbox_to_anchor=(1.1, 1))
axes = plt.gca()
plt.show()
plot_dataset(x_train0, y_train)
plot_dataset(x_test0, y_test)
def normalize(X):
### START YOUR CODE
mu = X.mean()
std = X.std()
X_norm = (X-mu)/std
return X_norm, mu, std
### END YOUR CODE
x_train,mu,stdev = normalize(x_train0)
x_test = (x_test0-mu)/stdev
Dummy recognition system that takes decisions randomly.
def dummy_predictor(x):
rnd = np.random.uniform(size=(1,x.shape[1]))
return np.round(rnd)
def pass_rate(x):
n_pass = np.sum(dummy_predictor(x_train))
rate = n_pass/x_train.shape[1]
return rate
Compute the performance $N_{correct}/N$ of this system on the test set ex1-data-train.csv, with $N$ the number of test samples and $N_{correct}$ the number of correct decision in comparison to the ground truth. This dummy recognition system should have a performance of ~50%
print(pass_rate(x_train.shape[1]))
0.57
performance_check = [pass_rate(x_train) for i in range(1000)]
print(np.mean(performance_check))
0.49917
We consider different models of different complexities involving different number of parameters. All these models involve combinations of powers in $x_1,x_2$ and are of the form
$\quad g(x_1,x_2) = \sigma(h(x_1,x_2)), \quad h(x_1,x_2)=\sum_{k=0}^n w_k \phi_k(x_1,x_2)$
with $\phi_k$ multinomials in $x_1,x_2$ (i.e. combinations of powers in $x_1,x_2$). The decision boundary is then given by $h(x_1,x_2)=0$. This can be formulated by a linear model of the form $\mathbf{W}\cdot\mathbf{x}$ by adding different dimensions to the input data with suitable powers of the prime input data $x_1,x_2$.
Specifically, we consider the following situations:
Linear Affine: $h(x_1,x_2) = b_0 + w_1x_1 + w_2x_2$ where $\mathbf{x}=(1,x_1,x_2)$
Quadratic: $h_2(x_1,x_2) = b_0 + w_1x_1 + w_2x_2 + w_3x_3 + w_4x_4 + w_5x_5$ where $\mathbf{x}=(1,x_1,x_2,x_1^2,x_2^2,x_1x_2)$
etc.
All the above models are linear in the parameters. We can use the same optimisation function.
The method polynomial_features below will help you to extend the input dataset by additional dimensions up to a given polynomial order.
def polynomial_features(x, order):
"""
Arguments:
x -- input data as numpy array of shape (2,m) where m is the number of samples
order -- the max order of terms to be added (x1^j*x2^i and i+j<=order)
Returns:
numpy array of shape (n,m) where n = (order+1)*(order+2)/2 (all the monomials x1^j*x2^i and i+j<=order)
"""
m = x.shape[1]
x1,x2 = x[0,:].reshape(1,m),x[1,:].reshape(1,m)
features = np.concatenate([np.ones((m),dtype='float').reshape(1,m),x1,x2]).reshape(3,m)
n = 3
if order > 1:
for i in range(2,order+1):
for term in range(i+1):
features = np.append(features, (x1**(i-term)*x2**term).reshape(1,m), axis=0)
n += 1
return features
def initialize_weights(n):
return np.random.normal(size=(1,n))*0.01
# Auxiliary methods suited for performing the optimize-step below
def predict(X,W):
"""
Computes the predicted value - given the inpute feature matrix of shape (n,m) and weights vector of shape (1,n).
The number of features n also includes the bias term.
"""
### START YOUR CODE
h = W.dot(X)
out = 1/ (1+np.exp(h))
return out
### END YOUR CODE
def cost(A,Y):
"""
Computes cross-entropy cost.
Arguments:
A -- Activations
Y -- Labels
"""
m = Y.shape[1]
c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
return c
def error_rate(A, Y):
"""
Computes the error rate.
Arguments:
A -- Activations
Y -- Labels
"""
Ypred = np.round(A)
return np.sum(Y != Ypred) / Y.size
def gradient_cost(A,X,Y):
"""
Computes the gradient for the cost with respect to the weights vector of size (1,n)
Arguments:
A -- Activations of shape (1,m)
X -- Input of shape (n,m)
Y -- Labels of shape (1,m)
Returns:
Vector of shape (n,m)
"""
### START YOUR CODE
grad = (Y-A).dot(X.T)
return grad
### END YOUR CODE
def optimize(Xtrain,Ytrain,Xtest,Ytest,nepochs,learningrate):
"""
Implements (batch) gradient descent for minimizing cross-entropy cost. It returns the learning curves
for cost and error rate (test and training). The curves are returned as numpy array of lenghth nepochs+1
(the +1 for the initial values).
Arguments:
Xtrain -- input data for training, numpy array with shape (n,m)
Ytrain -- labels for training, numpy array with shape (1,m)
Xtest -- input data for test, numpy array with shape (n,m)
Ytest -- labels for test, numpy array with shape (1,m)
nepochs -- number of epochs
learningrate -- learning rate
Returns:
traincosts -- learning curve with the cost on the training dataset, a numpy array of shape (nepochs+1)
testcosts -- learning curve with the cost on the test dataset, a numpy array of shape (nepochs+1)
trainerror -- learning curve with the error rate on the training dataset, a numpy array of shape (nepochs+1)
testerror -- learning curve with the error rate on the test dataset, a numpy array of shape (nepochs+1)
W -- parameter vector, a numpy array of shape (1,n+1)
"""
### START YOUR CODE
n = Xtrain.shape[0]
W = initialize_weights(n)
train_costs = []
train_errors = []
test_costs = []
test_errors = []
for i in range(nepochs):
# predict the train set
pred_train = predict(Xtrain, W)
# wheight update
W -= learningrate * gradient_cost(pred_train, Xtrain, Ytrain)
# train
train_costs.append(cost(pred_train, Ytrain))
train_errors.append(error_rate(pred_train, Ytrain))
# test
pred_test = predict(Xtest, W)
test_costs.append(cost(pred_test, Ytest))
test_errors.append(error_rate(pred_test, Ytest))
return np.array(train_costs), np.array(test_costs), np.array(train_errors), np.array(test_errors), W
### END YOUR CODE
def evaluate(x_train,y_train,x_test,y_test,pol_degree, nepochs, learningrate):
"""
Evaluate a model by training it, plotting the learning curves and the decision boundary and
returning the performance (final cost and error rate obtained for training and test set)
"""
Xtrain = polynomial_features(x_train, pol_degree)
Xtest = polynomial_features(x_test,pol_degree)
traincosts, testcosts, trainerror, testerror, W = optimize(Xtrain,y_train,Xtest,y_test,nepochs,learningrate)
plot_curves(traincosts, testcosts, trainerror, testerror)
Jtrain, Jtest, etrain, etest = traincosts[-1],testcosts[-1],trainerror[-1],testerror[-1]
print(Jtrain, Jtest, etrain, etest)
print(W)
return Jtrain, Jtest, etrain, etest, W
def plot_curves(traincosts, testcosts, trainerror, testerror):
iterations = range(traincosts.size)
f = plt.figure(figsize=(10,3))
plt.subplot(1,2,1)
plt.plot(iterations, traincosts,label="train")
plt.plot(iterations, testcosts, label="test")
plt.xlabel('Epochs')
plt.ylabel('Cost')
plt.legend()
plt.subplot(1,2,2)
plt.plot(iterations, trainerror, label="train")
plt.plot(iterations, testerror, label="test")
plt.ylabel('Error')
plt.xlabel('Epochs')
plt.show()
def plot_decision_boundary(x, y, W, pol_degree):
x1 = x[0,:]
x2 = x[1,:]
indices_pass = np.where(y[0,:]==1)
indices_fail = np.where(y[0,:]==0)
x1_pass = x1[indices_pass]
x2_pass = x2[indices_pass]
x1_fail = x1[indices_fail]
x2_fail = x2[indices_fail]
f, ax = plt.subplots(figsize=(7, 7))
ax.scatter(x1_fail,x2_fail,marker='o',color='green',label='not admitted')
ax.scatter(x1_pass,x2_pass,marker='x',color='red',label='admitted')
plt.xlabel('Exam 1 score $x_1$')
plt.ylabel('Exam 2 score $x_2$')
ax.legend(bbox_to_anchor=(1.1, 1))
axes = plt.gca()
x1_min, x1_max = x1.min() - 1, x1.max() + 1
x2_min, x2_max = x2.min() - 1, x2.max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, (x1_max-x1_min)/100), np.arange(x2_min, x2_max, (x2_max-x2_min)/100))
xx10 = xx1.reshape(1,xx1.size)
xx20 = xx2.reshape(1,xx2.size)
xx = np.concatenate((xx10, xx20), axis=0)
yy = W.dot(polynomial_features(xx, pol_degree)).reshape(xx1.shape)
ax.contour(xx1, xx2, yy, levels=[0], cmap=plt.cm.Paired)
#ax.axis('off')
plt.show()
Evaluate different polynomial models of the form as described above (starting with linear of order=1, then proceeding to quadratic of order=2 and to higher order models).
Use the "evaluate" function above that will provide also some diagnostic plot. Carefully tune the inputs such as the nepcohs and learning rate - do this for each selected model. Inspect the learning curves to judge whether the training has converged.
Remember the error rates for training set and test for the different models and create a plot showing the error rates at different model complexity.
Describe what you observe.
import itertools
pol_degree = list(range(1, 20, 2))
nepochs = [500, 1000]
learningrate = [0.01, 0.1, 0.2]
for pol_d, n_epochs, lr in itertools.product(pol_degree, nepochs, learningrate):
traincost1, testcost1, trainerror1, testerror1, W1 = evaluate(x_train, y_train, x_test, y_test,
pol_d, n_epochs, lr)
plot_decision_boundary(x_train, y_train, W1, pol_d)
plot_decision_boundary(x_test, y_test, W1, pol_d)
0.2035284039304025 0.2150584103716009 0.11 0.11 [[-1.68739413 -3.83697297 -3.74561364]]
0.20349770158944003 0.2148453653539708 0.11 0.11 [[-1.7198245 -3.90445628 -3.81433603]]
0.20349770158944003 0.21484536535397059 0.11 0.11 [[-1.7198245 -3.90445628 -3.81433603]]
0.20349778911011124 0.21485515996671223 0.11 0.11 [[-1.71808132 -3.90082616 -3.8106392 ]]
0.20349770158944008 0.2148453653539705 0.11 0.11 [[-1.7198245 -3.90445628 -3.81433603]]
0.20349770158944003 0.21484536535397059 0.11 0.11 [[-1.7198245 -3.90445628 -3.81433603]]
0.07379648134763711 0.13235408493916367 0.0 0.08 [[-5.10391065 -3.8739093 -3.16273569 2.42927413 1.49504784 1.90395244 -1.38188536 -1.75577284 -1.79873164 -1.96903358]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.06
[[-15.56262343 -12.33554989 -11.15873562 7.43953941 5.09775387
6.00607155 -3.18231357 -6.23982521 -6.52618125 -4.80099345]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.06
[[-26.39674139 -21.37739916 -18.89533449 11.88879734 7.24101062
9.40875924 -5.66113791 -10.4756101 -11.48593966 -9.19787787]]
0.05435165023782912 0.1310889200796841 0.0 0.07 [[-6.76824858 -5.25132758 -4.35114827 3.31784597 2.43317912 2.84154677 -1.47123884 -2.38261309 -2.45982766 -2.26716533]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.06
[[-18.12731595 -14.17358382 -12.93492309 8.71102931 6.32309783
7.30204054 -4.12061798 -7.428017 -6.97535493 -5.1725711 ]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.06 [[-32.09455678 -25.54644284 -23.4595653 15.70328794 9.21535086 10.53567614 -6.46268764 -10.99333465 -13.17142636 -11.32849751]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan 0.13813785359424283 0.0 0.06 [[-3.58292663 -3.10754455 -2.62646754 -0.94622978 3.81822285 -1.04333994 -1.50638888 -1.10952885 -1.25913565 -1.62964677 1.63689729 -0.03129824 0.81293913 0.12612751 1.67133723 -1.61346102 -1.40316307 -0.16335837 -0.39463931 -1.1171149 -1.68023366]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08
[[ -9.01504671 -8.33309258 -7.32675204 -9.34953907 15.30727055
-7.79205242 -4.87420299 -3.6766461 -4.0453655 -7.27503193
7.80233844 -3.65626696 5.04552911 -0.40761714 1.72672569
-12.37713748 -6.55629751 -1.67160471 -3.00144773 -4.06019512
-9.78646109]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08 [[-16.20645365 -16.16403622 -13.39420635 -20.618454 29.73372544 -16.79379972 -8.85636525 -6.5357883 -7.52842875 -13.26603008 13.49125104 -9.38132351 11.01797046 -2.04470314 2.52000818 -25.90920898 -11.40880429 -3.11302219 -6.38829334 -6.1741143 -20.52796426]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan 0.16243432221727122 0.0 0.06 [[-4.56609096 -3.97722318 -3.17256154 -1.04024012 5.09837963 -1.19185706 -1.85580166 -1.47541049 -1.61380882 -2.03167136 2.11916893 0.13234415 0.97648933 0.23410982 2.35004976 -2.03266444 -1.64281888 -0.13259347 -0.47811952 -1.27039051 -2.08011718]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08
[[-10.24374448 -9.01604865 -7.76392857 -8.69732565 15.94750749
-7.25484045 -5.80961354 -3.74523385 -4.10129122 -8.14654703
8.34295536 -3.5207888 4.49924124 0.16929298 2.46140292
-12.19960417 -6.60224011 -1.09946203 -2.80158348 -4.39454893
-9.62582553]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08 [[-16.89267197 -15.98822717 -13.65570532 -19.75552473 29.56755597 -15.94593619 -9.3472241 -6.42632273 -7.62995283 -14.08143644 14.77897166 -8.43442905 10.25207947 -1.33469659 2.93230084 -25.45385271 -11.98586792 -2.79305481 -6.39310714 -6.88406944 -19.69939525]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.06 [[-2.92877141 -2.66833409 -2.60012056 -1.30916758 3.77214838 -1.13225587 -1.32801252 -0.8547856 -1.19655347 -1.42308403 -0.69674311 1.96028449 -1.40096045 2.39878842 -1.10477022 -1.09733576 -1.24349591 0.02870859 -0.52569417 -0.65338953 -1.72260951 1.59287026 -0.74990428 1.38394978 -0.9061048 0.84199754 0.01173155 0.27731753 -3.02723473 -1.33020059 -0.49407079 -0.37992956 -0.47223123 -0.16769167 -0.64330285 -2.79434043]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08
[[ -8.42998644 -9.20506159 -8.72692619 -13.83511273 20.85536079
-11.93297517 -5.40960121 -3.13886161 -4.02868316 -8.72407122
-8.72919 12.34879292 -12.71024341 16.97413945 -13.83444069
-7.00296056 -7.95541677 1.82095039 -4.51624772 -1.98575617
-12.64252403 13.14130094 -11.85467856 12.87600108 -9.82267884
6.33436241 0.03337339 -5.29707362 -27.08079784 -8.0664921
-4.21976745 -2.44381863 -3.25400186 -1.9181353 -3.04867555
-23.84984712]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08 [[-16.09591535 -15.56207347 -18.63609926 -32.73717954 43.42305822 -25.25749845 -7.59341894 -7.8718729 -8.2505152 -16.04003481 -19.40118967 26.87972793 -24.56041299 34.28142198 -28.23869133 -16.94576707 -21.1894525 2.65005659 -11.08736482 -4.24704987 -23.74393858 34.51996644 -17.65718659 29.90889044 -17.50564624 14.94885802 -0.75612684 -9.05356188 -74.65934653 -33.72264555 -11.59650292 -13.16273652 -6.90614186 -6.52846561 -7.59380715 -43.43941647]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.06 [[-3.86739674 -3.35258711 -3.10391726 -1.19591035 4.59505684 -0.99873246 -1.67782244 -1.08140901 -1.59938757 -1.67659974 -0.4007408 2.18364661 -1.48818397 2.76324004 -0.87132332 -1.45655773 -1.63727556 0.01946535 -0.6153242 -0.91532643 -1.85859657 1.84110582 -0.50381934 1.36181611 -0.8321702 0.87932329 0.0696481 0.79877357 -3.73812979 -2.03212387 -0.56813861 -0.59409338 -0.48863597 -0.26933747 -0.74611332 -2.85674417]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08
[[ -9.19893304 -7.41077973 -8.88125623 -12.21781253 18.52838718
-9.44960603 -5.69837201 -3.22163587 -3.77283502 -9.46298312
-6.32681917 12.05183147 -11.48731331 15.64433772 -11.30479359
-8.49148649 -9.05070932 1.20803676 -4.59959562 -2.54235168
-12.21146556 15.56846092 -6.85853372 11.30037399 -6.87935979
5.20744318 0.65726392 -4.44914146 -30.4097948 -13.33430695
-4.88928175 -4.69215997 -3.48657081 -2.18379815 -4.38210828
-20.6551208 ]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08 [[-17.64872905 -17.02018592 -18.79417575 -31.14240495 45.14482737 -26.2488454 -9.18717375 -7.92957993 -7.20105401 -18.24397878 -18.19167005 29.24133534 -27.18398615 36.32126083 -28.67939342 -14.967648 -19.54913654 3.50034932 -11.14630915 -3.25211478 -25.742544 37.14146267 -17.60159821 28.32528622 -18.37618773 15.08558126 -1.39030782 -7.05822909 -64.21174295 -24.61986386 -12.82004706 -8.82895174 -7.97456058 -5.07157245 -6.97311408 -45.16147929]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08
[[ -2.48116713 -2.17536877 -2.8618503 -2.31869063 4.34729377
-1.42075993 -1.03768437 -0.8792285 -1.09119448 -1.66167281
-2.4484479 3.50841777 -3.04304189 4.01603609 -2.26062349
-0.95675647 -1.9634783 0.2459155 -0.65297147 -0.81239878
-1.55184913 -1.50816218 2.54711406 -1.74797484 2.44639969
-2.42666394 3.3611255 -2.41101711 -3.39878091 -4.08577375
0.64125413 -1.98456521 0.28562237 -0.77269344 -0.60616852
-2.52621235 3.74148346 -1.11939916 3.57361266 -1.7763605
2.35725717 -1.27242724 0.8536726 0.32077822 -0.06263567
-14.42618935 -8.00878446 -1.37742594 -3.18742716 -0.69146348
-0.82700381 -1.08033318 0.06216823 -1.18308248 -5.43722121]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.01 0.08
[[-10.03099885 -12.04955035 -12.56555529 -21.21735563 30.69258653
-16.4039995 -7.47519341 -3.83336206 -5.44974191 -11.75445814
-22.96253037 28.23248291 -27.4381241 31.86779674 -23.3876076
-6.35528447 -13.02109689 3.04673857 -5.40086963 -3.36827869
-14.15241553 -16.67963777 18.77790186 -15.91124697 18.6628622
-20.37370897 26.44506945 -27.18441846 -21.32746593 -25.85149309
5.92883726 -14.05687635 3.97230286 -7.90191511 -0.19942577
-24.97745488 24.06879895 -21.91970042 30.91607634 -23.87525169
24.34483171 -17.87484765 11.09731451 0.09633106 -17.52776009
-93.27081295 -41.90720034 -10.66250856 -15.37410137 -7.02755738
-3.19054108 -7.29933184 -1.97099036 -2.78340619 -48.01675265]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08
[[ -20.47180494 -19.97040085 -31.62836104 -57.7924136 73.41457998
-39.36537816 -7.23147406 -11.3131356 -11.82640284 -22.98311092
-57.54191628 68.89152879 -62.74726668 73.3157839 -54.63192825
-11.38538032 -34.61688558 7.08558468 -14.25371749 -7.33775118
-28.27658716 -28.64546561 54.84728878 -35.28646258 49.12263233
-47.38310227 60.28314852 -58.76894806 -62.22583981 -75.75067339
11.73225658 -38.91234857 9.04597808 -19.59870139 -2.66785725
-51.07926264 105.08828831 -14.42157708 79.12048589 -36.63743424
54.58741726 -31.35160381 23.62993623 -1.91545948 -27.99245478
-278.61324459 -151.57331275 -35.0217891 -58.75870891 -15.37967166
-17.24491665 -16.387937 -6.08456383 -14.23981221 -97.02180589]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08
[[ -3.34539873 -2.72491351 -3.38720617 -2.09523313 4.97478938
-1.27214086 -1.4996164 -1.02984736 -1.06122057 -2.33458582
-2.23581235 4.22599495 -3.42008187 4.53643515 -2.22509916
-1.06765577 -1.98975246 0.33806603 -0.79904074 -0.5807377
-2.2819446 -0.95615537 3.41191837 -2.06348977 2.92891728
-2.72165935 3.82373926 -2.75720903 -2.89219436 -3.6890174
0.62177927 -1.88887372 0.46600112 -0.99566654 -0.11350464
-3.30822831 6.35292268 -0.35438269 3.95661457 -1.68894502
2.66725345 -1.34197458 1.02671344 0.67683626 -1.3652041
-12.63332949 -6.45557367 -1.98957092 -2.30823867 -1.11766815
-0.42314501 -1.05716207 -0.12007766 -0.36625695 -5.7793903 ]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08
[[ -12.03948756 -11.41960581 -14.34897197 -24.96596656 34.64589873
-15.97586922 -6.43911024 -3.89273884 -8.15987962 -10.87174619
-23.99760166 30.55247822 -28.99858347 36.26564623 -24.10992515
-7.31469754 -15.65645712 3.38548208 -5.27431003 -6.63512009
-10.90007012 -13.84145994 22.02858671 -15.68964341 21.83570144
-22.19156048 30.58746023 -25.53128627 -29.66267643 -34.02474689
7.99820627 -17.29104585 4.34576283 -7.26311705 -4.34134136
-21.25457973 36.34033113 -13.58181856 35.19615912 -19.23240177
24.25126902 -13.95964185 9.50888206 2.02237487 -2.79992346
-128.30317988 -65.24305448 -7.54805699 -26.47252278 -2.78448908
-7.56791712 -6.91477252 -0.98876233 -7.63444184 -51.60172619]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08
[[ -21.36887861 -17.91733455 -32.7124721 -59.78417204 72.35052114
-36.92410816 -6.7525352 -12.07257149 -12.33045866 -23.63228659
-58.20739534 66.80557298 -61.40242942 72.96761128 -51.67309822
-14.8118977 -38.6053774 6.32756866 -15.71558751 -7.49295273
-27.99355731 -29.95740158 52.21453683 -32.12684226 48.27411522
-47.13195086 62.073999 -55.2999284 -76.57363516 -87.5770122
9.9758465 -44.63239481 9.10032116 -22.62851843 -1.45334208
-47.7512896 96.97326442 -15.10106813 85.00712294 -36.98965746
55.9217454 -28.78165402 20.0536933 5.81014018 -24.37085454
-325.78371916 -184.48270409 -41.01793345 -74.93858911 -15.13040666
-27.19864844 -13.20829037 -13.34665857 -8.38161036 -84.88420165]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.01 0.08
[[ -2.09142015 -2.29481434 -3.64972774 -3.96675327 6.51813264
-2.63749286 -0.34387313 -1.3021382 -1.05624268 -2.05508834
-4.914682 6.52415135 -5.4950843 6.6540203 -4.54002942
0.54624589 -2.72776668 0.83996649 -1.13990962 -0.74569262
-1.73996516 -4.85354341 7.5973033 -5.66103812 6.49741876
-5.92348747 6.88824759 -6.19528586 -0.20369178 -5.42184542
1.86845289 -3.0096903 1.1313083 -1.39378946 -0.45837885
-2.88040294 -0.0374275 8.24952801 -3.0538916 5.50820732
-3.67195596 4.81414151 -4.61928734 5.59352088 -6.66824715
-6.84406255 -10.71339713 1.07550206 -5.5518911 1.42091977
-2.7717712 0.57589653 -1.34270638 -0.42789367 -6.84237053
21.66988741 6.03360338 9.73916779 -0.98766815 6.73680557
-3.30614795 4.91654302 -2.8632608 2.18412911 -0.92761299
-3.09439966 -34.69827558 -22.215289 -7.61433336 -8.17708939
-3.57467671 -1.67514143 -3.4582226 1.40173587 -3.76006886
1.39390062 -2.38461018 -17.33304266]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.01 0.08
[[ -15.96613387 -17.09909161 -28.71624688 -49.97468868 67.73779291
-30.23006278 -2.3750624 -9.34811681 -13.27565875 -16.8284664
-56.35679765 66.26778134 -61.07443132 73.84263356 -48.42956539
2.93938562 -30.62980246 8.21245963 -9.04913247 -11.47997559
-11.63409539 -57.08339058 71.26422796 -58.29685202 67.98967175
-65.91396097 79.28384899 -62.53770037 -16.15440321 -67.86699944
23.55234749 -36.27968241 12.18204672 -12.36773975 -7.57670635
-17.05848729 -22.01204539 65.90384699 -26.37527612 49.81624512
-37.0168261 51.56628065 -53.63144202 70.69143383 -58.50768189
-123.60681324 -140.455912 21.93502076 -75.78636478 24.52261534
-38.83277984 11.07113739 -14.26335536 -2.0715812 -44.21069124
143.10705431 16.30988848 110.41880267 -31.1201055 77.17309545
-42.28694751 51.24735558 -25.34974784 14.33873636 11.73077259
6.17303986 -537.07189936 -299.14426369 -57.663663 -129.66425737
-10.14445135 -50.49269059 -12.55721145 -8.27247326 -21.17523081
5.42759033 -3.4792529 -122.43480934]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.01 0.08
[[ -32.77904451 -36.66714356 -57.33983906 -102.17183178
137.96695371 -62.50168648 -7.03676478 -17.70152434
-29.37954948 -32.29570334 -116.39237842 135.13984054
-124.63812317 149.91460817 -100.20571472 2.78274716
-59.7262779 14.16636239 -17.31539239 -26.42613679
-21.0169657 -120.06535498 146.50522867 -120.74697089
137.87046579 -133.39106982 159.24296958 -127.87822113
-35.53759627 -134.17078842 43.88197605 -71.03930906
21.38424526 -23.96762515 -20.51849423 -32.48374296
-51.58277199 136.34713954 -58.1301238 101.7387309
-76.27760793 102.17052462 -105.52398273 138.17050557
-114.90035985 -248.03408543 -281.72688014 41.19641937
-150.99691294 45.83665763 -76.36566481 17.78177029
-27.28998525 -15.14306308 -89.53795596 280.73516194
33.61116098 217.18598877 -63.38285369 154.30883665
-89.23983135 107.32365759 -60.20317579 40.35767921
9.96194567 29.82019525 -1063.80250795 -609.50179914
-111.67707379 -265.4649036 -19.74826788 -103.3814183
-27.81821724 -15.36440389 -50.39188204 14.51839975
-33.47711292 -251.71258597]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.01 0.08
[[ -3.08131181 -3.54069527 -3.95490157 -3.21422239 6.76634358
-1.91612011 -1.83986389 -0.79860251 -1.8880613 -2.17970443
-4.18224503 5.95868143 -5.36796614 6.79939054 -4.03399433
-0.76551561 -2.09469254 0.48699541 -0.34770005 -1.72409908
-1.3891226 -5.149599 5.85742705 -5.340984 5.70355324
-5.79746611 6.90326311 -5.90711899 -1.3794922 -4.21172042
2.2971578 -2.15089208 0.82388567 -0.40960528 -1.62526341
-2.08636287 -3.92429682 4.01650773 -3.09683728 3.45062621
-3.39076553 3.87094502 -4.51330597 5.48170792 -6.35948276
-7.71503765 -7.69481523 3.85684578 -4.31995046 2.63957416
-2.01461605 0.49983255 -0.20062741 -1.93677629 -5.96632721
5.19833654 -4.50518756 7.02708186 -5.54198784 6.01771378
-5.40553401 4.75730614 -3.79212828 2.15636399 -1.22969124
-2.11516755 -33.78947823 -13.3086249 2.64317882 -5.70711267
2.20480681 -1.57179732 -0.42882492 1.25400865 -2.93260727
2.26381791 -4.10768345 -17.43452073]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.01 0.04
[[ -15.80608261 -18.50045673 -14.19133795 -12.86344099 33.64176451
-13.00348888 -17.82908516 -2.14206178 -8.9606144 -15.30444056
-17.96287788 35.631593 -33.08996124 38.34631309 -25.71557993
-8.67790931 -9.43183528 2.75635129 -2.65986274 -8.92584786
-13.79154486 -27.48924164 37.94046272 -33.430879 32.8271082
-33.89033156 38.62596672 -41.33250104 -0.46615788 -15.45077998
9.970221 -9.92983755 4.62032006 -5.37692612 -7.45778525
-23.9264449 -25.05606871 28.274679 -21.70584871 18.60347919
-18.2528752 18.76301955 -24.16792395 28.54252503 -55.52510435
-1.63057145 -16.70977962 11.73708885 -12.98481518 8.23100479
-8.34387745 2.34225382 -6.86119731 -9.05956618 -58.86701911
23.58816199 -26.62761788 37.15633483 -42.80806837 44.77250175
-45.09745843 39.97325302 -35.12725943 19.6671543 -13.6817097
-64.24290757 -36.22183503 6.98114826 -12.85171379 8.75754031
-15.19159397 14.44118004 -19.9808961 17.14489085 -22.80229013
6.06493784 -27.93154456 -146.43064184]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.0 0.08 [[-2.86642526e+01 -2.78455398e+01 -4.14753594e+01 -7.00149486e+01 9.81450574e+01 -4.25336111e+01 -1.06436823e+01 -8.73779376e+00 -2.69647054e+01 -2.61863814e+01 -7.82200378e+01 9.89883600e+01 -8.79945839e+01 1.07431303e+02 -7.63415004e+01 -3.29578011e+00 -3.45055274e+01 3.19280461e+00 -1.00008544e+01 -2.56997116e+01 -1.91934941e+01 -7.71863976e+01 1.16680575e+02 -8.77127383e+01 1.00286121e+02 -9.24871991e+01 1.09151125e+02 -1.05679311e+02 -1.97555908e+01 -8.09107375e+01 1.56143727e+01 -4.09688522e+01 4.90383314e+00 -1.41110173e+01 -2.34452483e+01 -3.98513493e+01 -6.37512018e+00 1.29455943e+02 -4.51499958e+01 8.42257792e+01 -5.36071329e+01 7.13651142e+01 -6.88189443e+01 8.24733492e+01 -1.11056277e+02 -1.27204839e+02 -1.80711310e+02 -5.34535083e-01 -8.94304223e+01 8.22631472e+00 -3.89767439e+01 -6.04134951e+00 -1.22217839e+01 -2.81579784e+01 -1.20191859e+02 3.15776122e+02 1.07206921e+02 1.54451855e+02 -1.21361659e+01 1.11686388e+02 -5.68339086e+01 8.69842320e+01 -5.73420903e+01 4.74988355e+01 -4.02201536e+01 -3.46857074e+01 -5.57211823e+02 -4.25184461e+02 -1.33123073e+02 -1.66829473e+02 -6.49428913e+01 -4.08683598e+01 -6.62883435e+01 2.46700522e+01 -8.07201371e+01 3.50897272e+01 -6.93757183e+01 -3.45934694e+02]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h))
nan nan 0.02 0.09 [[-1.35356627e+00 -5.40595645e+00 -5.23258366e+00 -7.00143544e+00 1.19153183e+01 -4.60762651e+00 -1.11248333e+00 -9.17355131e-01 -3.72796062e+00 -1.61744394e+00 -9.08173897e+00 1.05952504e+01 -9.87963146e+00 1.24543122e+01 -8.77845655e+00 1.21654638e+00 -3.33484893e+00 7.37514139e-01 -1.66503322e-01 -3.57877919e+00 2.38247430e-01 -1.17063107e+01 1.16202527e+01 -1.11628133e+01 1.16192885e+01 -1.18772319e+01 1.39065185e+01 -1.28870674e+01 2.09490313e+00 -7.31405624e+00 4.32656430e+00 -3.67812270e+00 1.26228985e+00 -7.90679518e-02 -3.63065188e+00 2.70395609e-01 -1.38195032e+01 1.23299668e+01 -1.16146736e+01 1.13635504e+01 -1.14063560e+01 1.19102906e+01 -1.29654085e+01 1.47941583e+01 -1.64861605e+01 -2.19006551e+00 -1.38981719e+01 8.19905493e+00 -8.53068122e+00 5.48937408e+00 -4.25633841e+00 1.45631212e+00 -9.73400202e-02 -3.96985537e+00 -3.34983452e+00 -1.14016707e+01 8.51141193e+00 -6.94792910e+00 6.65198190e+00 -6.34955785e+00 6.24475835e+00 -7.07717624e+00 8.12166237e+00 -1.04376592e+01 1.22174212e+01 -1.76100787e+01 -2.53493743e+01 -2.54529420e+01 1.06878264e+01 -1.47475601e+01 9.00968301e+00 -8.30923954e+00 4.72401851e+00 -3.15227395e+00 -7.11646347e-02 6.59082827e-01 -4.88249492e+00 -1.64518208e+01 8.84379154e+00 -1.14671132e+01 1.65618548e+01 -1.47244634e+01 1.53813618e+01 -1.55360973e+01 1.43966679e+01 -1.35326376e+01 1.08846920e+01 -8.35548211e+00 3.50621620e+00 -2.39759279e+00 -8.45662260e+00 -1.13959858e+02 -4.71680462e+01 2.98956969e+00 -2.01375973e+01 6.15512865e+00 -7.55637494e+00 8.54534096e-01 6.99929579e-01 -4.89988064e+00 6.33197071e+00 -9.61279147e+00 6.66218949e+00 -9.01213652e+00 -5.58969488e+01]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.02 0.09 [[-1.03153138e+01 -3.01625220e+01 -3.48673302e+01 -4.91649127e+01 7.22597912e+01 -2.33972736e+01 -1.24548715e+01 -4.35481712e+00 -1.66053198e+01 -2.74536031e+01 -6.78673262e+01 7.66057992e+01 -7.22210462e+01 8.06699060e+01 -4.42290344e+01 3.23884675e-01 -2.20237049e+01 3.09304792e+00 -1.61078477e+00 -1.58385202e+01 -2.31218355e+01 -9.19856063e+01 8.88730793e+01 -8.29596430e+01 8.48614229e+01 -8.55955268e+01 9.30509847e+01 -6.91602157e+01 6.93097360e-01 -5.40688565e+01 2.36544203e+01 -2.53562064e+01 7.44993954e+00 -4.18311560e+00 -1.28006631e+01 -2.87954260e+01 -1.15586209e+02 9.89143279e+01 -8.49033378e+01 8.53360062e+01 -8.32430167e+01 8.65278078e+01 -9.27920432e+01 1.02032360e+02 -9.61754574e+01 -4.51995084e+01 -1.16421955e+02 4.58952392e+01 -6.31211875e+01 3.28048150e+01 -3.24792667e+01 1.27305803e+01 -1.03140850e+01 -5.92596181e+00 -5.44137544e+01 -1.14789298e+02 8.21826705e+01 -4.15913878e+01 4.95063061e+01 -3.94720181e+01 4.26500506e+01 -4.70063408e+01 5.57942583e+01 -7.33201698e+01 9.09617370e+01 -1.22787347e+02 -2.58203029e+02 -2.49175458e+02 5.35240022e+01 -1.22016372e+02 5.18471107e+01 -6.44286269e+01 2.95079555e+01 -2.99021820e+01 9.31438696e+00 -1.41967294e+01 3.43153990e+00 -1.19651670e+02 -5.14664285e+00 -2.80962031e+01 1.60842084e+02 -1.12887236e+02 1.46396465e+02 -1.34065240e+02 1.33625550e+02 -1.20664689e+02 1.00799942e+02 -7.60732322e+01 3.17358690e+01 1.04434509e+01 -1.35696092e+02 -1.04915182e+03 -5.61315581e+02 -2.86590076e+01 -2.07507116e+02 1.68204478e+01 -7.04931903e+01 -1.30443385e+01 2.53599698e+00 -4.28410563e+01 3.56948887e+01 -5.05375830e+01 2.06906898e+01 -8.78315642e+00 -2.57047180e+02]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.04 0.07 [[-1.21459798e+01 -9.37968606e+01 -6.62817804e+01 -9.70847754e+01 1.84975216e+02 -7.31277689e+01 -3.64550168e+01 -2.92008713e-02 -7.32808819e+01 -7.64141534e+00 -1.28126548e+02 1.64624329e+02 -1.49086402e+02 1.91377775e+02 -1.45479289e+02 -1.81924666e+00 -2.12514467e+01 -9.01558417e+00 1.45131102e+01 -7.16378621e+01 2.23207632e+01 -1.70492484e+02 1.86745688e+02 -1.73454225e+02 1.78653692e+02 -1.79840688e+02 2.11361814e+02 -2.12707841e+02 2.71480012e+01 -4.92208184e+01 3.08377599e+01 -2.10015542e+01 -5.57663391e+00 1.78726676e+01 -7.45087043e+01 1.62953020e+01 -1.97329228e+02 2.05816036e+02 -1.90211483e+02 1.82260317e+02 -1.77858268e+02 1.82877410e+02 -1.95945466e+02 2.22054186e+02 -2.70990338e+02 3.85794367e+01 -7.93065768e+01 7.02343823e+01 -6.04743208e+01 3.96056807e+01 -2.38875181e+01 -6.76761405e+00 1.69990503e+01 -7.94665800e+01 -6.72007923e+01 -1.28145532e+02 1.51720836e+02 -1.36770756e+02 1.20098526e+02 -1.09494775e+02 1.05131438e+02 -1.12129124e+02 1.25292867e+02 -1.57980471e+02 1.78002810e+02 -2.90615114e+02 -1.98533095e+01 -8.45195634e+01 9.28651228e+01 -8.60382156e+01 6.49673085e+01 -4.65758728e+01 1.99422567e+01 -3.11515831e+00 -3.20928830e+01 2.08799572e+01 -8.74893150e+01 -3.55542903e+02 2.87959786e+02 -1.87862367e+02 1.84101032e+02 -2.00910078e+02 2.11770137e+02 -2.17965675e+02 2.09840387e+02 -1.94719079e+02 1.59456265e+02 -1.22832741e+02 5.07255015e+01 -5.43383663e+01 -1.45321747e+02 -3.31616813e+02 4.77886508e+01 7.74610956e+00 -1.20114342e+01 -1.30237945e+01 3.86497035e+01 -7.21605809e+01 9.64836091e+01 -1.27778639e+02 1.34891420e+02 -1.68024967e+02 7.95192579e+01 -1.17037304e+02 -1.22128950e+03]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.01 0.09
[[ -2.69710519 -3.87823369 -5.50276605 -6.51512045 10.96423149
-3.98889128 -0.30068011 -1.77660626 -2.44370007 -2.72500959
-8.63506082 10.69753994 -9.40972466 11.72836127 -7.90949266
1.68806649 -4.1614976 1.29822641 -1.40082852 -2.13396612
-1.11419895 -10.81575114 13.35690189 -11.25464294 11.92591183
-11.21567185 13.11388459 -11.70382949 2.43938691 -8.51447123
3.63965166 -4.65272418 1.73799995 -1.60864882 -1.86329078
-1.36180374 -10.11514769 17.38364114 -12.09904654 13.68944724
-11.64440593 12.50680543 -12.16685627 13.98207529 -14.61224862
-1.74783416 -17.05380095 4.93245 -9.75490917 4.07978512
-5.16661101 1.68012129 -1.75776466 -1.75572226 -5.17225717
3.35897597 21.95688743 -6.61939858 13.19236457 -7.26527658
9.44288318 -7.48786393 9.00522869 -9.32449524 11.4569118
-14.13081482 -24.52588066 -36.35282123 0.79554611 -18.24792014
3.42392095 -8.86829376 1.69046838 -3.21961815 -0.88710664
-0.38682167 -2.68601775 -17.59730731 62.35167487 25.7893813
22.4376986 2.74714723 15.1641546 -6.738369 13.36056878
-9.56659018 10.89021212 -7.6519951 5.85918091 -3.25438701
-1.04835004 -113.12950341 -85.68708245 -25.69313673 -33.4893693
-10.23834633 -9.5075165 -9.78750103 3.25254915 -12.42803777
9.85695096 -14.16928539 8.91858779 -8.5004827 -53.01515313]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.02 0.07 [[-2.00848417e+01 -4.13033722e+01 -2.41099123e+01 -3.26360282e+01 6.77367072e+01 -2.71599010e+01 -2.77763039e+01 -3.80527327e+00 -1.38918285e+01 -2.72555583e+01 -5.47455208e+01 7.51517150e+01 -7.25328100e+01 7.80201776e+01 -4.36312731e+01 -1.04402670e+01 -1.95897214e+01 3.30027519e+00 -7.25730906e-01 -1.46292919e+01 -2.52293316e+01 -8.52831872e+01 8.35526383e+01 -8.35117564e+01 8.21246568e+01 -8.55041586e+01 9.09326908e+01 -6.86763667e+01 -4.09773040e+00 -4.32296888e+01 2.39216684e+01 -2.21900994e+01 7.70784264e+00 -3.60537342e+00 -1.22410776e+01 -3.17793838e+01 -1.19188961e+02 8.14143150e+01 -8.88314260e+01 7.93700398e+01 -8.39136549e+01 8.32908012e+01 -9.21527702e+01 9.92586779e+01 -9.77227829e+01 -3.41490717e+01 -8.25320579e+01 5.01416002e+01 -5.33405479e+01 3.32796061e+01 -2.92389800e+01 1.26016645e+01 -9.14677817e+00 -6.67131449e+00 -5.91356386e+01 -1.50183068e+02 2.73072821e+01 -5.70680133e+01 3.36556616e+01 -4.34884069e+01 3.53698761e+01 -4.66912990e+01 5.09322876e+01 -7.13239171e+01 8.58663567e+01 -1.29823109e+02 -1.84845151e+02 -1.46907275e+02 7.65376691e+01 -9.42494825e+01 5.64506824e+01 -5.45751526e+01 2.92922139e+01 -2.55995843e+01 7.97694197e+00 -1.09219293e+01 -1.19691387e+00 -1.30088508e+02 -1.61860578e+02 -2.02550822e+02 1.05856219e+02 -1.59560286e+02 1.31232373e+02 -1.52538813e+02 1.31754515e+02 -1.31898697e+02 1.04233696e+02 -8.51556717e+01 3.80382520e+01 -1.54472423e+00 -1.57498134e+02 -7.28796512e+02 -2.43698000e+02 6.74432834e+01 -1.26752569e+02 4.12076895e+01 -4.37301542e+01 -9.05783517e+00 1.42535527e+01 -4.50256863e+01 4.33718632e+01 -5.57620705e+01 3.03519057e+01 -2.48318124e+01 -2.86400769e+02]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.02 0.07 [[-3.81789638e+01 -1.08759783e+02 -7.58990713e+01 -8.40768524e+01 1.95877169e+02 -6.16251358e+01 -6.12976251e+01 -5.25282396e+00 -7.75462687e+01 -2.34053799e+01 -1.14991663e+02 1.68967032e+02 -1.58478218e+02 2.10147552e+02 -1.38353106e+02 -1.42596827e+01 -4.53203057e+01 -1.52226824e+00 8.30152019e+00 -7.19501804e+01 1.43947416e+01 -1.78032938e+02 1.76121900e+02 -1.73551233e+02 1.82093841e+02 -1.93250872e+02 2.38962801e+02 -2.09126390e+02 2.20691394e+01 -1.02744272e+02 5.29344535e+01 -4.57457930e+01 7.76144313e+00 7.02974756e+00 -6.56157901e+01 2.28706629e+01 -2.62147646e+02 1.64925959e+02 -1.74883168e+02 1.61896142e+02 -1.75234588e+02 1.87268746e+02 -2.13404375e+02 2.64554207e+02 -2.58559840e+02 6.98538756e+00 -1.91846717e+02 1.11099426e+02 -1.11921811e+02 6.56149586e+01 -5.31020238e+01 1.26553475e+01 5.09866924e-01 -5.14861034e+01 -2.85958863e+01 -3.69093037e+02 4.56316329e+01 -9.17493823e+01 4.70689581e+01 -6.91940040e+01 6.35850024e+01 -9.53321395e+01 1.23938148e+02 -1.74836368e+02 2.47316358e+02 -2.33326948e+02 -1.98956966e+02 -3.26953405e+02 1.52326684e+02 -1.76686301e+02 9.28752581e+01 -8.64887593e+01 3.30764770e+01 -2.39097113e+01 -1.32884268e+01 3.47049996e+00 -2.14734109e+01 -2.29764752e+02 -5.11048347e+02 -4.42663850e+02 2.87804548e+02 -4.00608965e+02 3.46182376e+02 -3.77301139e+02 3.26835729e+02 -3.07318532e+02 2.34352450e+02 -1.60975750e+02 6.05658748e+01 5.82937795e+01 6.39408044e+01 -1.06813215e+03 -5.02481629e+02 6.51020633e+01 -1.57576960e+02 -3.21828317e+01 2.88402079e+01 -1.36977985e+02 1.43914163e+02 -1.99707753e+02 1.86534150e+02 -1.94737334e+02 9.91216092e+01 1.39379521e+01 -8.39014851e+02]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h))
nan nan 0.07 0.09 [[-4.56587862e+00 -1.04872137e+01 -4.15718065e+00 -1.58243256e+01 2.46078606e+01 -1.46781998e+01 -1.79734994e+00 -4.23004953e-01 -8.46827622e+00 3.06321925e+00 -2.03738719e+01 2.31077741e+01 -2.13443710e+01 2.64317463e+01 -2.26868898e+01 2.84539133e+00 -5.02901088e+00 -4.17670896e-01 1.69211678e+00 -8.97638132e+00 7.10262632e+00 -2.63747701e+01 2.75444921e+01 -2.50663652e+01 2.67394659e+01 -2.66216283e+01 3.09438449e+01 -3.10686762e+01 5.60846276e+00 -1.26580767e+01 5.79092894e+00 -5.20244037e+00 -2.54682636e-01 2.70042022e+00 -1.01638113e+01 9.25250438e+00 -3.26797644e+01 3.53856435e+01 -2.95703910e+01 3.10997295e+01 -2.90202325e+01 3.09147876e+01 -3.18928257e+01 3.62760507e+01 -3.99990483e+01 2.27164882e+00 -2.67462185e+01 1.23525856e+01 -1.53588105e+01 7.53677735e+00 -6.27231125e+00 -1.50724368e-01 3.19125520e+00 -1.16767703e+01 7.56767023e+00 -3.23226105e+01 4.50175590e+01 -2.92434384e+01 3.49702090e+01 -2.83758624e+01 3.08043351e+01 -2.92250839e+01 3.21777729e+01 -3.47556580e+01 3.97833190e+01 -4.81268843e+01 -2.49989634e+01 -5.59045888e+01 1.70538442e+01 -3.25589834e+01 1.55166577e+01 -1.83237888e+01 8.59978600e+00 -7.00905735e+00 -7.81249577e-01 3.41687979e+00 -1.31397275e+01 -4.73761059e+00 -3.18280899e-02 5.23090966e+01 -5.30186677e+00 2.89824946e+01 -1.00172136e+01 1.84740615e+01 -1.17477089e+01 1.64838442e+01 -1.64562539e+01 2.19466698e+01 -2.76822899e+01 3.34983503e+01 -4.85488669e+01 -1.39092834e+02 -1.24171837e+02 6.68385202e+00 -6.45218301e+01 1.84659650e+01 -3.51447521e+01 1.33557359e+01 -1.65780080e+01 4.20582416e+00 -3.00071325e+00 -6.14854119e+00 5.42650585e+00 -1.46879640e+01 -4.74983716e+01 1.48884655e+02 4.85272799e+01 1.02292876e+02 -1.36072672e+01 7.05173202e+01 -3.83353719e+01 5.91003108e+01 -4.48110500e+01 4.98659521e+01 -3.97468797e+01 3.53258386e+01 -2.39651652e+01 1.14936592e+01 -6.66711544e+00 -1.51484489e+01 -5.53610411e+02 -3.03039181e+02 -7.12179156e+01 -1.28763029e+02 -9.23022427e+00 -5.46229623e+01 -4.85980829e+00 -1.44588196e+01 -1.43922502e+01 1.01949890e+01 -2.56094625e+01 2.44689700e+01 -3.34281204e+01 2.01149839e+01 -2.00268828e+01 -1.78104442e+02]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h))
nan nan 0.04 0.07 [[-1.13896827e+01 -9.53521462e+01 -1.95332241e+01 -8.20800356e+01 1.60242094e+02 -8.85528300e+01 -3.17116310e+01 9.43684590e+00 -7.56819493e+01 3.34650451e+01 -1.07361287e+02 1.37104677e+02 -1.27112681e+02 1.70073077e+02 -1.49429583e+02 1.13172016e+00 -1.41105998e+01 -1.71806057e+01 2.46698076e+01 -7.80064364e+01 6.47156446e+01 -1.42307783e+02 1.58490267e+02 -1.47194978e+02 1.58255514e+02 -1.61792268e+02 1.97147258e+02 -2.09878373e+02 2.69520786e+01 -4.61725590e+01 1.91293968e+01 -1.20193642e+01 -1.77367719e+01 3.29610980e+01 -8.64044028e+01 7.76392225e+01 -1.79072253e+02 1.99900872e+02 -1.77543323e+02 1.79342949e+02 -1.73795991e+02 1.84619553e+02 -1.97255505e+02 2.31000973e+02 -2.74799023e+02 4.29286865e+01 -9.34650054e+01 5.60665631e+01 -5.58407560e+01 2.51995132e+01 -1.33473145e+01 -2.09038946e+01 3.85846012e+01 -9.74510532e+01 5.17988024e+01 -1.84707909e+02 2.44818443e+02 -1.96579976e+02 1.99102329e+02 -1.77614659e+02 1.80824064e+02 -1.81055510e+02 1.96935594e+02 -2.22356544e+02 2.56842990e+02 -3.39183079e+02 2.15822130e+01 -1.64904874e+02 9.28709821e+01 -1.12426907e+02 6.40765347e+01 -5.93908462e+01 2.13618312e+01 -7.55574494e+00 -3.24844308e+01 4.22951924e+01 -1.06713743e+02 -8.13293677e+01 -4.96590263e+01 2.41561266e+02 -1.32429540e+02 1.56888895e+02 -1.03381505e+02 1.09308483e+02 -9.22980648e+01 1.04476399e+02 -1.18231241e+02 1.47904291e+02 -1.96825542e+02 2.27145590e+02 -3.60852965e+02 -1.32869486e+02 -2.67533772e+02 1.00514377e+02 -1.75450630e+02 7.49261200e+01 -8.69690930e+01 2.23165293e+01 -1.47934811e+01 -3.17251249e+01 4.13531753e+01 -8.38965110e+01 5.59947327e+01 -1.08067485e+02 -5.26034663e+02 5.58645383e+02 2.49825452e+01 2.35747440e+02 -1.27188117e+02 2.54895168e+02 -2.21719275e+02 2.73040183e+02 -2.50821047e+02 2.50963190e+02 -2.14016817e+02 1.66761847e+02 -1.04017981e+02 7.47034104e+00 -1.41214353e+01 -1.54189953e+02 -7.19626522e+02 -3.85554956e+02 -3.96133336e+01 -1.89878537e+02 -5.29552884e+01 -3.11831695e+00 -1.41226992e+02 1.39504914e+02 -2.27755427e+02 2.35854399e+02 -2.87783341e+02 2.74127522e+02 -3.01289093e+02 1.46463321e+02 -9.88448562e+01 -1.88995670e+03]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.03 0.09 [[-2.58473378e+01 -6.43509546e+01 -1.64816591e+02 -2.35493748e+02 3.00172602e+02 -1.01372494e+02 1.07474927e+01 -3.34293956e+01 -5.30056628e+01 -1.02828502e+02 -3.22116647e+02 3.35638187e+02 -3.00772731e+02 3.35272883e+02 -2.01625501e+02 5.78243694e+01 -8.72028601e+01 2.24811663e+01 -2.05465482e+01 -5.05634858e+01 -7.01055415e+01 -4.38966395e+02 4.45695601e+02 -3.91882338e+02 3.96495025e+02 -3.73455072e+02 3.99262945e+02 -3.14074568e+02 8.97460169e+01 -1.92560653e+02 8.57852646e+01 -9.89638051e+01 3.26281882e+01 -2.41841153e+01 -4.66974026e+01 -7.05883618e+01 -5.65281994e+02 6.20303120e+02 -4.96370183e+02 5.08971247e+02 -4.53503897e+02 4.61533710e+02 -4.47495639e+02 4.75994746e+02 -4.46432735e+02 4.78970995e+01 -4.09584464e+02 1.47157238e+02 -2.34110266e+02 1.13278368e+02 -1.27355161e+02 5.09374398e+01 -4.31199618e+01 -3.44574083e+01 -1.33089654e+02 -5.77912279e+02 8.65418903e+02 -5.34196322e+02 6.30293037e+02 -4.86659345e+02 5.12812150e+02 -4.58346021e+02 4.82107796e+02 -4.86435013e+02 5.38194016e+02 -6.11308448e+02 -3.10177990e+02 -9.14123970e+02 1.53330207e+02 -4.88713956e+02 1.83053788e+02 -2.83388819e+02 1.35191872e+02 -1.57705539e+02 7.07768933e+01 -7.99308525e+01 -4.85108494e+00 -3.34860494e+02 -1.34419818e+01 1.19195374e+03 -2.07261658e+02 6.45299898e+02 -2.48851986e+02 3.89623765e+02 -2.33257151e+02 2.95222085e+02 -2.58629595e+02 3.28162771e+02 -3.81676006e+02 5.03402246e+02 -8.21257165e+02 -1.86754923e+03 -2.22510015e+03 -1.40715820e+02 -1.02099320e+03 1.31576982e+02 -5.15437741e+02 1.26729050e+02 -2.52192095e+02 6.94911449e+01 -1.17607802e+02 3.24886291e+01 -1.02837576e+02 2.68500003e+01 -8.50998745e+02 2.72203300e+03 1.68964498e+03 1.45761989e+03 2.20717516e+02 9.68440703e+02 -3.85790446e+02 8.40829133e+02 -6.08248593e+02 7.52791760e+02 -6.01911637e+02 5.70219683e+02 -3.79912257e+02 1.92291519e+02 1.17981359e+02 -1.07075765e+03 -7.73526087e+03 -5.95192121e+03 -1.66592165e+03 -2.25357582e+03 -4.99963419e+02 -8.22263095e+02 -3.27848599e+02 -1.24847404e+02 -3.90504893e+02 2.22960677e+02 -4.36608938e+02 3.16292015e+02 -3.49095264e+02 9.48629312e+01 -9.65876447e+01 -2.03908439e+03]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.04 0.1 [[-7.84494355e-01 -9.99059758e+00 -1.01411038e+01 -1.37625207e+01 2.35325702e+01 -8.37391912e+00 -1.07718785e+00 -1.66727368e+00 -7.77940852e+00 -2.25682215e+00 -1.85260975e+01 2.16494066e+01 -1.93246541e+01 2.49220322e+01 -1.75112533e+01 3.48328399e+00 -5.43353986e+00 6.23733754e-01 4.13848174e-02 -7.61478680e+00 2.74269508e+00 -2.48413273e+01 2.69427250e+01 -2.39827475e+01 2.51467068e+01 -2.43033201e+01 2.86241177e+01 -2.64137047e+01 7.07098700e+00 -1.18240045e+01 6.07950280e+00 -5.69987816e+00 8.69850402e-01 7.32991982e-01 -8.12802551e+00 5.23796863e+00 -3.10537989e+01 3.62900112e+01 -2.96628384e+01 3.07642019e+01 -2.81331393e+01 2.92971255e+01 -2.93576684e+01 3.31347701e+01 -3.52402204e+01 7.13062291e+00 -2.31330581e+01 1.16998525e+01 -1.42540814e+01 7.41285278e+00 -6.66526925e+00 9.74411509e-01 1.04875201e+00 -9.06276160e+00 3.48374130e+00 -2.93817406e+01 4.92322401e+01 -3.19904972e+01 3.73102671e+01 -2.99547612e+01 3.16317105e+01 -2.92192850e+01 3.10265410e+01 -3.23385262e+01 3.60197785e+01 -4.28344457e+01 -7.99838676e+00 -4.54975029e+01 1.54467000e+01 -2.83794094e+01 1.36513858e+01 -1.65118704e+01 7.83757545e+00 -7.06883285e+00 1.35583290e-01 1.16895899e+00 -1.02061068e+01 -9.01163755e+00 7.92934446e+00 6.44134492e+01 -1.45462466e+01 3.76905337e+01 -1.75006312e+01 2.51623822e+01 -1.71910660e+01 2.06929003e+01 -1.92716822e+01 2.28908420e+01 -2.66418705e+01 3.00058251e+01 -4.27929895e+01 -8.03408707e+01 -9.53634603e+01 5.65375024e+00 -5.36401222e+01 1.42388410e+01 -2.93963753e+01 1.00920045e+01 -1.38490047e+01 2.77511816e+00 -2.68259601e+00 -5.50231859e+00 3.22573161e+00 -1.17828460e+01 -5.16199529e+01 1.72679442e+02 7.90757706e+01 7.56268648e+01 1.10061938e+01 4.79936235e+01 -1.66715235e+01 3.97558990e+01 -2.68761214e+01 3.41108774e+01 -2.62461899e+01 2.43010721e+01 -1.62807801e+01 7.30524999e+00 -7.11385309e+00 -1.03920011e+01 -3.54740825e+02 -2.21228503e+02 -6.36265867e+01 -1.02243176e+02 -1.51105999e+01 -4.17847397e+01 -1.05840580e+01 -8.20453460e+00 -1.76021918e+01 1.22367237e+01 -2.61281587e+01 2.36027474e+01 -3.17677360e+01 1.71121301e+01 -1.71542328e+01 -1.81460812e+02]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.02 0.09 [[-2.41648221e+01 -5.14562676e+01 -1.11310985e+02 -1.46043134e+02 1.95643201e+02 -5.41737863e+01 -4.82754529e+00 -2.14654539e+01 -3.06376887e+01 -8.27522735e+01 -2.05160201e+02 2.22726637e+02 -2.01866493e+02 2.18989247e+02 -1.13124498e+02 3.06937102e+01 -5.91337980e+01 1.68952995e+01 -1.22058285e+01 -3.01037854e+01 -6.33119643e+01 -2.83867728e+02 2.89606666e+02 -2.60724829e+02 2.61786944e+02 -2.49684029e+02 2.59765098e+02 -1.82857891e+02 5.39734536e+01 -1.30118085e+02 6.57782613e+01 -6.65408299e+01 2.36415998e+01 -1.42947045e+01 -2.79105665e+01 -6.22735901e+01 -3.71450934e+02 3.90250474e+02 -3.27362172e+02 3.30285067e+02 -3.02152257e+02 3.03571220e+02 -2.97840432e+02 3.04664710e+02 -2.63408660e+02 2.04419342e+01 -2.68460234e+02 1.24256332e+02 -1.60676447e+02 8.70368703e+01 -8.54767584e+01 3.51709958e+01 -2.53938380e+01 -2.09367084e+01 -9.61253191e+01 -4.05370597e+02 5.17440153e+02 -3.53384492e+02 3.95477706e+02 -3.22558011e+02 3.30939109e+02 -3.04752838e+02 3.12730823e+02 -3.20626736e+02 3.30387903e+02 -3.59098918e+02 -2.41154401e+02 -5.67574287e+02 1.71721320e+02 -3.32563077e+02 1.60223868e+02 -1.97624684e+02 1.05559544e+02 -1.05936307e+02 4.68366054e+01 -4.63701796e+01 -5.74063930e+00 -2.05354102e+02 -1.48831312e+02 6.42272032e+02 -1.59748660e+02 3.70761808e+02 -1.71359413e+02 2.33832430e+02 -1.56329972e+02 1.82754388e+02 -1.69703040e+02 1.98955758e+02 -2.42701706e+02 2.68570345e+02 -4.69893631e+02 -1.32989380e+03 -1.29073932e+03 9.26373505e+01 -6.71303986e+02 1.95587611e+02 -3.67207420e+02 1.43512245e+02 -1.87498442e+02 7.10405652e+01 -8.17948834e+01 1.81501011e+01 -5.31764906e+01 3.39981233e-01 -4.76102835e+02 1.20244993e+03 7.08036048e+02 8.30637776e+02 2.78574675e+00 5.89655191e+02 -3.09931143e+02 5.35571450e+02 -4.25684465e+02 4.91153293e+02 -4.17493516e+02 3.85770119e+02 -2.98278542e+02 1.58073260e+02 -8.27116981e+01 -5.72087162e+02 -5.28364039e+03 -3.23129173e+03 -5.79312027e+02 -1.39664259e+03 -3.36837463e+01 -5.90906808e+02 -3.76998583e+01 -1.52858930e+02 -1.50563276e+02 9.56242490e+01 -2.39619756e+02 1.94912975e+02 -2.41486995e+02 9.84611006e+01 -1.26605501e+02 -1.06472598e+03]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h))
nan nan 0.04 0.11 [[ 9.54957300e+00 -2.37543733e+02 -2.32506304e+02 -3.32646856e+02 5.62609628e+02 -2.23479799e+02 -1.40240231e+01 -5.65604588e+01 -1.49101121e+02 -7.60499077e+01 -4.65423681e+02 5.40462535e+02 -4.90173273e+02 6.03268224e+02 -4.23052956e+02 1.19107422e+02 -1.67692953e+02 5.13303867e+01 -2.14222621e+01 -1.45139248e+02 2.17036223e+01 -6.37043826e+02 6.69642270e+02 -6.01852201e+02 6.27478629e+02 -6.11537828e+02 7.01410127e+02 -6.25546518e+02 2.28461984e+02 -3.51883439e+02 2.09732080e+02 -1.82229655e+02 6.68829418e+01 -1.26120939e+01 -1.53895618e+02 7.02243683e+01 -8.25970611e+02 8.87454317e+02 -7.37991637e+02 7.59945728e+02 -6.98887857e+02 7.27074978e+02 -7.32338017e+02 8.16385568e+02 -8.30897182e+02 2.41608520e+02 -6.73273339e+02 3.90830727e+02 -4.18838417e+02 2.58447785e+02 -2.15234830e+02 7.94481122e+01 -1.17170849e+01 -1.72137677e+02 3.34954112e+01 -8.86794523e+02 1.16791312e+03 -7.85502234e+02 8.97230772e+02 -7.27068829e+02 7.64572781e+02 -7.09731691e+02 7.56800166e+02 -7.93791661e+02 8.85691172e+02 -1.00611496e+03 -1.78064715e+02 -1.29410447e+03 5.56167802e+02 -7.95221018e+02 4.60904510e+02 -4.78951334e+02 2.78916043e+02 -2.26856102e+02 6.30857400e+01 -4.02497508e+00 -2.01825543e+02 -2.15387848e+02 -2.74885024e+02 1.42847625e+03 -3.36679623e+02 8.35303268e+02 -3.76759557e+02 5.40845398e+02 -3.60433108e+02 4.42539168e+02 -4.15748767e+02 5.13188726e+02 -6.15186247e+02 7.22210653e+02 -9.90625277e+02 -2.25015418e+03 -2.64353086e+03 4.40739065e+02 -1.42533221e+03 5.48231451e+02 -7.94506580e+02 3.64396081e+02 -3.82575195e+02 1.29495127e+02 -8.25365964e+01 -1.06480157e+02 9.17129028e+01 -2.82907751e+02 -1.04243966e+03 2.84859958e+03 1.46500120e+03 1.91980166e+03 -2.36514600e+01 1.34425760e+03 -6.73766123e+02 1.18556443e+03 -9.05694400e+02 1.06035395e+03 -8.70574392e+02 8.08285072e+02 -5.89813226e+02 3.42664037e+02 -2.47484716e+02 -1.84020606e+02 -1.01759321e+04 -5.97899716e+03 -1.03743884e+03 -2.52075659e+03 -7.34822480e+01 -9.88478704e+02 -1.56265924e+02 -1.19288509e+02 -4.55286399e+02 4.28231103e+02 -7.42221351e+02 7.40665571e+02 -9.17679065e+02 6.32653326e+02 -6.17628882e+02 -3.48624039e+03]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h))
nan nan 0.11 0.1 [[-7.68709582e+00 -2.06186868e+01 4.50097919e+00 -1.64088969e+01 3.15620371e+01 -2.41992164e+01 -7.34121829e+00 3.18940410e+00 -1.51324504e+01 1.37741652e+01 -2.21606226e+01 2.74345312e+01 -2.57326229e+01 3.36263554e+01 -3.37741194e+01 -2.62609392e-01 -1.55185036e+00 -4.44199803e+00 6.66205152e+00 -1.60164266e+01 1.96207845e+01 -3.07375283e+01 3.11678704e+01 -2.95982985e+01 3.19015222e+01 -3.27320800e+01 3.96048861e+01 -4.37062954e+01 5.26067863e+00 -7.43902773e+00 2.99488187e+00 -5.77733891e-01 -4.76698455e+00 8.74033539e+00 -1.78658053e+01 2.44730273e+01 -4.31218548e+01 3.86416015e+01 -3.67204381e+01 3.64431878e+01 -3.59398376e+01 3.81860592e+01 -4.05698469e+01 4.76941008e+01 -5.46425795e+01 9.23184599e+00 -1.59774948e+01 1.15246036e+01 -8.78484289e+00 4.31493106e+00 -6.40069274e-01 -5.08156864e+00 1.03864454e+01 -1.96586600e+01 2.72589924e+01 -5.97175370e+01 4.61734528e+01 -4.42031007e+01 4.19212477e+01 -4.01451987e+01 4.01585651e+01 -4.07220485e+01 4.39467152e+01 -4.83758832e+01 5.67377158e+01 -6.54130474e+01 6.25034556e+00 -2.90128898e+01 2.31603287e+01 -1.98477668e+01 1.49768436e+01 -1.08841415e+01 5.84904027e+00 -1.29655075e+00 -4.83039760e+00 1.06717502e+01 -1.91130487e+01 2.28094741e+01 -7.86305738e+01 4.21922438e+01 -4.24479476e+01 3.82514960e+01 -3.55166670e+01 3.40515665e+01 -3.35777866e+01 3.51363736e+01 -3.82111242e+01 4.41010053e+01 -5.21827427e+01 6.29123588e+01 -6.95646185e+01 -2.50949993e+01 -4.78036857e+01 3.82230896e+01 -3.44313543e+01 2.77647771e+01 -2.22241749e+01 1.63568719e+01 -1.13628947e+01 6.00272293e+00 -1.33625619e+00 -4.06121447e+00 8.02354764e+00 -1.06959607e+01 -5.40403957e+00 -9.18994309e+01 -1.00283859e+01 -4.68955671e-01 -4.90284725e+00 7.12554320e+00 -8.82266806e+00 9.05663435e+00 -7.38789141e+00 3.84819588e+00 2.46249768e+00 -1.15095505e+01 2.41735206e+01 -3.96554425e+01 5.34116728e+01 -4.24745107e+01 -1.57664701e+02 -6.92644626e+01 5.00160120e+01 -4.67744602e+01 3.60271668e+01 -2.70252074e+01 1.81661966e+01 -1.08462506e+01 4.19707110e+00 7.81225800e-01 -4.92289864e+00 6.93372450e+00 -7.42638662e+00 1.35549152e+00 1.93019929e+01 -1.08767213e+02 -7.85219093e+01 -2.21177773e+02 1.74874778e+02 -1.78308137e+02 1.74536713e+02 -1.72384828e+02 1.68552035e+02 -1.63018460e+02 1.55181080e+02 -1.44410329e+02 1.30112887e+02 -1.11324948e+02 8.72763973e+01 -5.80178135e+01 2.67910762e+01 -1.41433813e+01 1.03712589e+02 -6.30735068e+02 -6.82240047e+01 2.27978897e+01 -2.54737551e+01 5.76553005e+00 1.09118323e+01 -2.74669594e+01 4.13881745e+01 -5.33698877e+01 6.21192739e+01 -6.79923975e+01 6.96098980e+01 -6.71035328e+01 5.77876714e+01 -3.97290579e+01 -1.84577695e+00 1.04555438e+02 -4.47744044e+02]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.09 0.09 [[-1.84102344e+02 -1.53743695e+02 7.07796656e+01 -2.31644152e+02 3.28621251e+02 -2.75545451e+02 -5.76826059e+01 5.12654444e+01 -1.59305668e+02 1.61716916e+02 -2.68940747e+02 2.95952895e+02 -2.85166478e+02 3.52356653e+02 -3.62802216e+02 -1.97589297e+01 3.95728650e+00 -6.56911859e+01 8.98664702e+01 -1.80320390e+02 2.21494637e+02 -3.41456024e+02 3.48606073e+02 -3.24670656e+02 3.44258567e+02 -3.52063186e+02 4.15243676e+02 -4.60119572e+02 -1.09664240e+01 -5.96758639e+01 -1.12227435e+01 1.90481837e+01 -7.99594239e+01 1.16551325e+02 -2.09101247e+02 2.70974859e+02 -4.39355228e+02 4.57939606e+02 -3.97371343e+02 4.08868157e+02 -3.89644990e+02 4.11998664e+02 -4.30438243e+02 4.99255801e+02 -5.67671694e+02 -7.12023576e+01 -1.79242736e+02 3.69403835e+01 -7.16929090e+01 -7.88896967e+00 2.17273381e+01 -9.27818602e+01 1.39054213e+02 -2.39991869e+02 2.97512208e+02 -5.09427814e+02 6.22128084e+02 -4.59290416e+02 5.06402545e+02 -4.37674929e+02 4.56383478e+02 -4.41102766e+02 4.73730506e+02 -5.04712017e+02 5.89295999e+02 -6.68550033e+02 -3.71104707e+02 -4.52319666e+02 6.94136870e+01 -2.37554665e+02 7.01304303e+01 -1.05548175e+02 6.51771982e+00 1.13017137e+01 -9.73220231e+01 1.48323437e+02 -2.54315856e+02 2.42398726e+02 -3.39989964e+02 8.39776846e+02 -3.61540000e+02 5.79905913e+02 -3.84670703e+02 4.55025761e+02 -3.83322946e+02 4.22107940e+02 -4.19043043e+02 4.76396666e+02 -5.28164401e+02 6.40758975e+02 -6.89242326e+02 -1.50325894e+03 -1.15806081e+03 -4.16768233e+00 -6.00711100e+02 1.49017166e+02 -3.34937141e+02 1.21159024e+02 -1.62867241e+02 3.65216118e+01 -1.80068091e+01 -8.38084761e+01 1.22123240e+02 -2.01834322e+02 -8.01168372e+01 8.14597163e+02 1.12416339e+03 3.92493760e+02 4.65335155e+02 1.10592952e+02 1.87823204e+02 2.59392046e+01 9.82724985e+01 -4.05143392e+01 1.28034223e+02 -1.60993813e+02 2.74800875e+02 -3.64577217e+02 5.13189833e+02 -3.65563541e+02 -5.47733570e+03 -3.14406332e+03 -5.72482502e+02 -1.47925573e+03 1.20585419e+02 -8.00093375e+02 2.20096128e+02 -4.41460546e+02 1.58921355e+02 -2.11808100e+02 5.14856805e+01 -4.40042012e+01 -6.09078729e+01 2.71022978e+01 4.74710406e+01 -1.23956211e+03 5.46939769e+03 1.66807046e+03 3.38955091e+03 -2.79044397e+02 2.09409526e+03 -9.68090488e+02 1.61399499e+03 -1.15346864e+03 1.35326137e+03 -1.10958989e+03 1.10574190e+03 -9.03637787e+02 7.74132632e+02 -5.32150394e+02 3.76297502e+02 -2.33734795e+02 1.21306476e+03 -1.90797460e+04 -9.08218730e+03 -3.12505654e+03 -3.75878505e+03 -5.36455343e+02 -1.78739011e+03 5.02868451e+01 -8.62547279e+02 8.33959287e+01 -3.49186243e+02 -3.26461488e+01 -4.21728469e+01 -1.49356225e+02 1.06023670e+02 -1.54899085e+02 -1.45666314e+02 8.34392158e+02 -5.00646882e+03]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h))
nan nan 0.09 0.1 [[-2.71095631e+02 -3.35417565e+02 1.15762761e+02 -4.00417077e+02 6.30568551e+02 -5.22313739e+02 -1.09827905e+02 7.96945889e+01 -2.95376152e+02 2.90020297e+02 -4.89325286e+02 5.60206878e+02 -5.41854668e+02 6.77610451e+02 -7.06827426e+02 -9.46945577e-02 -1.70062734e+01 -9.51894646e+01 1.50038389e+02 -3.29608069e+02 3.99768417e+02 -6.41747799e+02 6.54266301e+02 -6.16853010e+02 6.54492179e+02 -6.77450386e+02 7.98367876e+02 -9.08499410e+02 7.04865634e+01 -1.42025683e+02 3.58042261e+01 7.59103991e+00 -1.16658908e+02 2.00376933e+02 -3.89052890e+02 4.85806759e+02 -8.53777832e+02 8.53592001e+02 -7.61192962e+02 7.70959386e+02 -7.46654152e+02 7.84394958e+02 -8.31377646e+02 9.52758524e+02 -1.13715494e+03 6.01397253e+01 -3.56942700e+02 1.76087613e+02 -1.66861890e+02 4.82902775e+01 1.70117154e+01 -1.46674538e+02 2.52903466e+02 -4.72219964e+02 5.19843810e+02 -1.05935969e+03 1.15157528e+03 -9.07920784e+02 9.51131680e+02 -8.50146896e+02 8.63727560e+02 -8.49073051e+02 8.97551198e+02 -9.72916858e+02 1.09900894e+03 -1.37112815e+03 -2.72677662e+02 -8.05974920e+02 3.40515134e+02 -4.60396323e+02 2.46899234e+02 -2.14483971e+02 6.35998388e+01 2.48491833e+01 -1.89690716e+02 3.09253218e+02 -5.76764952e+02 3.80967757e+02 -9.53910822e+02 1.54150016e+03 -8.25439742e+02 1.09385404e+03 -8.07961539e+02 8.68243392e+02 -7.66257660e+02 7.99415377e+02 -8.05796535e+02 8.79818584e+02 -9.98726291e+02 1.11156639e+03 -1.48817467e+03 -1.83180435e+03 -1.90162100e+03 4.45563719e+02 -1.05260019e+03 5.07063511e+02 -5.97488427e+02 3.18278604e+02 -2.58302341e+02 5.49362479e+01 6.20701215e+01 -2.79960845e+02 3.80765574e+02 -6.98079851e+02 -3.08315527e+02 6.03237581e+02 2.04280389e+03 2.77305393e+02 9.12248648e+02 -7.07572133e+01 4.12150704e+02 -1.08655405e+02 2.21908055e+02 -1.40392829e+02 2.24852045e+02 -2.82738767e+02 4.08233111e+02 -5.98006464e+02 6.01704522e+02 -1.01388141e+03 -7.81788653e+03 -4.92679105e+03 7.21892031e+00 -2.40394450e+03 7.35248881e+02 -1.31043251e+03 5.98304717e+02 -6.54419330e+02 2.62335250e+02 -1.54372189e+02 -1.34681136e+02 2.86033067e+02 -5.79024969e+02 5.51604568e+02 -8.34816454e+02 -2.69726155e+03 7.61568186e+03 3.01889708e+03 4.96949995e+03 -3.56661023e+02 3.05440472e+03 -1.58923033e+03 2.49928793e+03 -1.98831001e+03 2.26903875e+03 -2.02964048e+03 2.03524491e+03 -1.83256671e+03 1.65291038e+03 -1.43120736e+03 1.14001944e+03 -1.62510292e+03 1.75773090e+03 -2.93828493e+04 -1.40712625e+04 -3.05346174e+03 -5.81638784e+03 2.90900559e+02 -2.70991210e+03 5.66211880e+02 -1.11952331e+03 1.07732868e+02 -7.55741291e+01 -5.19294140e+02 7.23257442e+02 -1.15797232e+03 1.34058752e+03 -1.72410076e+03 1.21223931e+03 -1.00245198e+03 -1.03428669e+04]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h))
nan nan 0.04 0.07 [[-2.04652871e+00 -2.60637718e+01 -2.07718972e+00 -1.61296275e+01 3.73960689e+01 -2.14286913e+01 -9.66492244e+00 3.08216265e+00 -1.93135259e+01 1.15662280e+01 -2.23950665e+01 3.04820941e+01 -2.81195561e+01 3.90056960e+01 -3.51864889e+01 -5.26262410e-01 -2.34256626e+00 -5.18800740e+00 7.67175484e+00 -2.01007735e+01 2.02802148e+01 -3.14938094e+01 3.38373153e+01 -3.22374108e+01 3.52773589e+01 -3.65863227e+01 4.54996311e+01 -4.88424896e+01 7.17430857e+00 -9.82062197e+00 4.26207127e+00 -9.21007076e-01 -5.76799030e+00 1.07800505e+01 -2.29693285e+01 2.70703217e+01 -4.47081071e+01 4.20525110e+01 -3.99923949e+01 3.99274839e+01 -3.99378436e+01 4.26907700e+01 -4.60181123e+01 5.46835429e+01 -6.39738377e+01 1.36872966e+01 -2.09081840e+01 1.52601371e+01 -1.11991496e+01 5.51943907e+00 -3.93084396e-01 -7.18603800e+00 1.39907926e+01 -2.74334581e+01 3.06892752e+01 -6.33729993e+01 5.26941464e+01 -4.98771582e+01 4.73650482e+01 -4.56877670e+01 4.55772732e+01 -4.67074647e+01 5.01688525e+01 -5.58479662e+01 6.51643960e+01 -8.08513227e+01 1.40611674e+01 -3.83527260e+01 3.07490344e+01 -2.56889184e+01 1.91637560e+01 -1.31264612e+01 6.10608681e+00 6.45745955e-01 -9.75048844e+00 1.75297318e+01 -3.29530861e+01 2.48494250e+01 -8.86266296e+01 5.82277784e+01 -5.56991082e+01 5.04758310e+01 -4.68236991e+01 4.43107197e+01 -4.34350998e+01 4.42665397e+01 -4.73806975e+01 5.27957587e+01 -6.21048972e+01 7.21690040e+01 -9.58799747e+01 -1.18503745e+01 -6.63340818e+01 5.34067935e+01 -4.67835305e+01 3.74451853e+01 -2.89721553e+01 2.01945810e+01 -1.20571963e+01 3.10134910e+00 5.03649696e+00 -1.59143535e+01 2.21479746e+01 -3.86337540e+01 -1.07777901e+01 -1.21653579e+02 3.41440075e+01 -3.66035423e+01 2.80316830e+01 -2.22306386e+01 1.72617063e+01 -1.43916339e+01 1.32162945e+01 -1.46814820e+01 1.83345961e+01 -2.58334876e+01 3.55927004e+01 -5.15019893e+01 5.97429741e+01 -9.05860431e+01 -1.32641129e+02 -1.10478676e+02 8.35706994e+01 -7.50247447e+01 5.89902757e+01 -4.47181019e+01 3.05257253e+01 -1.78984988e+01 5.55743275e+00 5.24916903e+00 -1.64236248e+01 2.48359296e+01 -3.65313631e+01 3.31374874e+01 -4.31557327e+01 -1.37713613e+02 -1.69110608e+02 -9.49506652e+01 7.09702111e+01 -8.39171198e+01 9.08654708e+01 -9.81843019e+01 1.02735451e+02 -1.05456689e+02 1.04920003e+02 -1.01708877e+02 9.42143854e+01 -8.33258688e+01 6.57452854e+01 -4.63901163e+01 1.81544391e+01 -2.49161668e+01 8.64451456e+00 -5.69949971e+02 -1.75244193e+02 1.09535800e+02 -1.00025783e+02 6.80868630e+01 -3.97193220e+01 1.16729938e+01 1.32386868e+01 -3.64709829e+01 5.63826522e+01 -7.45254133e+01 8.85400103e+01 -1.01454929e+02 1.05772841e+02 -1.13138169e+02 7.48305029e+01 -4.56111125e+01 -5.43331814e+02]]
<ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h))
nan nan 0.07 0.1 [[-6.01652967e+01 -2.43260592e+02 -6.53295060e+01 -3.07415257e+02 5.09120636e+02 -3.04378576e+02 -5.07682178e+01 4.38708802e+00 -1.84865615e+02 9.11479696e+01 -4.16976061e+02 4.80042823e+02 -4.43718764e+02 5.50258640e+02 -4.71602735e+02 6.01388697e+01 -7.93593401e+01 -2.10991092e+01 5.59230016e+01 -1.98421153e+02 1.92222077e+02 -5.71062048e+02 5.84737015e+02 -5.42194778e+02 5.68408952e+02 -5.66993867e+02 6.57242373e+02 -6.45463917e+02 1.55673274e+02 -2.09450839e+02 1.04538299e+02 -7.12522894e+01 -2.54179836e+01 8.79715442e+01 -2.29495642e+02 2.78132809e+02 -7.79668685e+02 7.83938751e+02 -6.97298225e+02 6.98943155e+02 -6.66522882e+02 6.93052886e+02 -7.10818819e+02 8.04537318e+02 -8.36188947e+02 2.16105527e+02 -4.31459308e+02 2.49555457e+02 -2.46472832e+02 1.29772222e+02 -7.96047011e+01 -3.15755290e+01 1.16093948e+02 -2.69380657e+02 3.44263482e+02 -1.00004881e+03 1.07760156e+03 -8.72241915e+02 8.93072889e+02 -7.97662471e+02 8.05105611e+02 -7.79790359e+02 8.19976357e+02 -8.60451402e+02 9.76063043e+02 -1.03026214e+03 9.14500746e+01 -8.56255644e+02 4.19214244e+02 -5.31212627e+02 3.16569912e+02 -3.02586741e+02 1.59554801e+02 -9.54999183e+01 -3.61481092e+01 1.35475227e+02 -2.99798812e+02 3.41206176e+02 -1.01209300e+03 1.45089626e+03 -9.02984081e+02 1.07369022e+03 -8.29986943e+02 8.71718250e+02 -7.73274924e+02 8.04888075e+02 -7.99765260e+02 8.77041129e+02 -9.56846116e+02 1.11932519e+03 -1.14490353e+03 -7.92534418e+02 -1.77302956e+03 5.28827727e+02 -1.04598507e+03 5.25853023e+02 -6.38841654e+02 3.67247695e+02 -3.47638157e+02 1.70907200e+02 -9.91012552e+01 -4.96712114e+01 1.39070246e+02 -2.78764466e+02 1.04325480e+02 3.17350077e+00 1.83983473e+03 -2.11053663e+02 9.90773102e+02 -3.32752684e+02 5.78931000e+02 -3.13202740e+02 4.13153743e+02 -3.36084178e+02 4.32654359e+02 -4.85869460e+02 6.52564478e+02 -8.12650353e+02 1.05659796e+03 -8.69235712e+02 -4.45318139e+03 -4.00656630e+03 1.55218462e+02 -2.06022411e+03 5.90730593e+02 -1.15141382e+03 4.82327674e+02 -6.09742932e+02 2.58091647e+02 -2.40089799e+02 3.02175394e+01 2.55154359e+01 -1.60271030e+02 1.55282302e+02 -1.19400927e+02 -8.67721104e+02 4.83740704e+03 2.15707785e+03 3.07838490e+03 -9.07442197e+01 2.04065238e+03 -1.00167958e+03 1.73916826e+03 -1.32714590e+03 1.57243713e+03 -1.33203048e+03 1.31893494e+03 -1.06133198e+03 8.50742194e+02 -4.71406605e+02 1.40409412e+02 2.08351551e+02 8.74395721e+02 -1.78001710e+04 -1.00723900e+04 -2.37682040e+03 -4.24182450e+03 -2.54029556e+02 -1.85452390e+03 -4.31706349e+01 -6.00895628e+02 -2.98758910e+02 1.53681853e+02 -6.11875768e+02 6.04923208e+02 -8.19105677e+02 7.78905104e+02 -7.81568781e+02 4.10332139e+02 3.37515320e+02 -4.10781378e+03]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.07 0.1 [[-1.14921773e+02 -4.79180616e+02 -1.37516242e+02 -6.08070403e+02 1.00685151e+03 -6.04877279e+02 -9.49659402e+01 1.55298786e+00 -3.63719959e+02 1.70412850e+02 -8.27693723e+02 9.49477318e+02 -8.78798418e+02 1.08654174e+03 -9.44496932e+02 1.26369562e+02 -1.65868829e+02 -3.66141933e+01 1.02849562e+02 -3.91156460e+02 3.65115549e+02 -1.13632800e+03 1.15827861e+03 -1.07505861e+03 1.12303655e+03 -1.12346330e+03 1.29310305e+03 -1.30415977e+03 3.17909909e+02 -4.26868070e+02 2.13654410e+02 -1.50166068e+02 -4.64947620e+01 1.66533779e+02 -4.56013515e+02 5.20567847e+02 -1.55396442e+03 1.55398062e+03 -1.38369132e+03 1.38159844e+03 -1.31924888e+03 1.36446815e+03 -1.40676495e+03 1.57111872e+03 -1.71661018e+03 4.40329246e+02 -8.72844340e+02 5.02535364e+02 -5.00142116e+02 2.61236935e+02 -1.64843376e+02 -6.40635630e+01 2.25319454e+02 -5.45955405e+02 6.14638187e+02 -1.99534703e+03 2.13563048e+03 -1.73014052e+03 1.76368574e+03 -1.57652072e+03 1.58253435e+03 -1.53515732e+03 1.60035067e+03 -1.69578204e+03 1.87551453e+03 -2.18560191e+03 1.94360801e+02 -1.72625923e+03 8.40004744e+02 -1.06978177e+03 6.30551314e+02 -6.06787871e+02 3.10687765e+02 -1.86646295e+02 -9.03651366e+01 2.76678458e+02 -6.38132355e+02 5.15877077e+02 -2.02084649e+03 2.87155099e+03 -1.78520687e+03 2.11290868e+03 -1.63208716e+03 1.70221272e+03 -1.50861985e+03 1.55529032e+03 -1.54831087e+03 1.67097096e+03 -1.86243924e+03 2.06677206e+03 -2.62542233e+03 -1.56701910e+03 -3.56657595e+03 1.05602071e+03 -2.09987158e+03 1.04150739e+03 -1.27336445e+03 7.12638642e+02 -6.75800290e+02 3.02087010e+02 -1.59894617e+02 -1.68955327e+02 3.27797640e+02 -6.87584366e+02 -1.89484384e+02 6.03612931e+00 3.63029604e+03 -3.89822537e+02 1.92521532e+03 -6.21030093e+02 1.09103749e+03 -5.64801792e+02 7.40105794e+02 -5.84835440e+02 7.43679883e+02 -8.50315793e+02 1.11111913e+03 -1.50295178e+03 1.69763282e+03 -2.62109636e+03 -8.87559080e+03 -8.04558546e+03 3.02556741e+02 -4.12917898e+03 1.15732526e+03 -2.28807208e+03 9.18486360e+02 -1.17454266e+03 4.36763520e+02 -3.93726441e+02 -7.10322761e+01 1.93139522e+02 -5.38870011e+02 4.96279143e+02 -6.29052459e+02 -2.72520279e+03 9.66505316e+03 4.23097499e+03 6.20927668e+03 -2.77097076e+02 4.15529398e+03 -2.11660324e+03 3.58235098e+03 -2.79981703e+03 3.29488014e+03 -2.86526711e+03 2.85708608e+03 -2.41984764e+03 2.01293886e+03 -1.42937897e+03 6.02419253e+02 -6.38344447e+02 -5.56168534e+02 -3.55450160e+04 -2.01978692e+04 -4.77357466e+03 -8.49311761e+03 -5.58027292e+02 -3.67515290e+03 -1.77085665e+02 -1.11119572e+03 -7.49414109e+02 4.77674479e+02 -1.46962159e+03 1.49479526e+03 -2.03178889e+03 2.00308410e+03 -2.20226787e+03 1.40803319e+03 -4.46592585e+02 -1.07386241e+04]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.12 0.09 [[-1.11990479e+01 -2.81609883e+01 8.79163950e+00 -1.86629405e+01 3.64960070e+01 -2.93284442e+01 -1.54537520e+01 6.25252365e+00 -1.97315962e+01 1.88151428e+01 -2.66808804e+01 3.38958615e+01 -2.98483523e+01 3.78852467e+01 -3.92932042e+01 -9.74773876e+00 3.65381476e+00 -1.08123334e+01 1.15197900e+01 -2.12628136e+01 2.49678252e+01 -3.91438783e+01 4.06403906e+01 -3.62792839e+01 3.84968022e+01 -3.82437301e+01 4.44905149e+01 -5.01280189e+01 -6.28895649e+00 1.63519283e+00 -6.44484570e+00 7.10080994e+00 -1.24507739e+01 1.55719060e+01 -2.50388113e+01 3.01900321e+01 -5.76369840e+01 5.44566053e+01 -4.89202683e+01 4.78162168e+01 -4.54490857e+01 4.67358520e+01 -4.80950430e+01 5.38445375e+01 -6.30837019e+01 -3.64254422e+00 -5.87162899e-01 -3.11455372e+00 3.84651825e+00 -7.66482326e+00 1.00115440e+01 -1.55438043e+01 2.00225574e+01 -3.05082999e+01 3.31022571e+01 -8.35762739e+01 7.53533552e+01 -6.76480525e+01 6.33816369e+01 -5.87417446e+01 5.68311458e+01 -5.53115108e+01 5.64534791e+01 -5.93295726e+01 6.48914839e+01 -7.82860867e+01 -1.19157240e+00 -3.53424439e+00 3.03755586e-01 5.47200886e-01 -3.74779539e+00 5.83653866e+00 -9.97389836e+00 1.34297406e+01 -1.98423289e+01 2.48312745e+01 -3.71057730e+01 2.72449145e+01 -1.15270317e+02 1.00882643e+02 -8.99904533e+01 8.20110636e+01 -7.45757727e+01 6.95489682e+01 -6.54197335e+01 6.34651549e+01 -6.31286840e+01 6.48788212e+01 -6.98357587e+01 7.43604624e+01 -9.26833779e+01 8.40026011e-01 -7.11244613e+00 4.06440005e+00 -3.07980897e+00 1.66524707e-02 2.09204301e+00 -5.74391719e+00 8.80628317e+00 -1.36776673e+01 1.79112587e+01 -2.56063973e+01 2.89613701e+01 -4.26842462e+01 -8.40500104e+00 -1.39147986e+02 1.17412840e+02 -1.02804559e+02 9.08272597e+01 -8.03842788e+01 7.22772936e+01 -6.57435422e+01 6.12791594e+01 -5.86883035e+01 5.81786668e+01 -6.04677827e+01 6.41569882e+01 -7.26153048e+01 7.13261758e+01 -9.11896759e+01 -1.42630135e+00 -8.24246074e+00 5.47068404e+00 -4.48525924e+00 1.23688541e+00 1.06323651e+00 -4.86768952e+00 7.96127433e+00 -1.23980795e+01 1.62326141e+01 -2.19488028e+01 2.59923933e+01 -3.47728518e+01 2.98227891e+01 -3.96465773e+01 -1.39368432e+02 -1.02470734e+02 7.45835324e+01 -5.84574313e+01 4.44298237e+01 -3.29433689e+01 2.37114524e+01 -1.66873549e+01 1.16483173e+01 -8.95140574e+00 8.26514341e+00 -1.05575341e+01 1.48445082e+01 -2.37109730e+01 3.18415401e+01 -4.56566104e+01 2.00185521e+01 -1.28418460e+01 -2.71532465e+01 9.82970118e+00 -1.10876948e+01 1.10783725e+01 -1.42786265e+01 1.67828944e+01 -2.11405489e+01 2.47295695e+01 -2.96448568e+01 3.36627966e+01 -3.88503279e+01 4.26509725e+01 -4.85257217e+01 4.90401008e+01 -5.64958084e+01 2.24582699e+01 -2.46806607e+00 -5.71561174e+02 1.65961297e+02 -1.88916056e+02 1.94710813e+02 -2.00122062e+02 2.02609867e+02 -2.03968570e+02 2.02976842e+02 -2.00860615e+02 1.96389605e+02 -1.90432025e+02 1.81530834e+02 -1.70634700e+02 1.55232482e+02 -1.37911462e+02 1.13194246e+02 -9.59251339e+01 7.71957295e+01 -1.95183171e+02 3.72293168e+02 -1.56112343e+02 1.18897375e+02 -1.12191532e+02 1.06249055e+02 -1.06487429e+02 1.07124209e+02 -1.11380796e+02 1.15274199e+02 -1.21291077e+02 1.26302236e+02 -1.32422196e+02 1.36686589e+02 -1.41518856e+02 1.42265185e+02 -1.44997563e+02 1.31566069e+02 -1.26911976e+02 -9.66728413e-01 1.55884622e+02 -1.96155786e+03]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.11 0.11 [[-1.26772500e+02 -2.40922438e+02 1.07689788e+02 -1.84135116e+02 3.37822036e+02 -3.07095434e+02 -1.03348077e+02 5.86368047e+01 -1.68811369e+02 1.85018102e+02 -2.46032626e+02 3.02103847e+02 -2.93289620e+02 3.61851580e+02 -3.99027232e+02 -2.92658483e+01 7.62559915e+00 -6.46324486e+01 9.25591108e+01 -1.79364097e+02 2.29319710e+02 -3.48190554e+02 3.49326680e+02 -3.34972068e+02 3.51774537e+02 -3.68142458e+02 4.27655957e+02 -5.00009970e+02 2.74741593e+01 -4.86580971e+01 4.73108642e+00 2.16628502e+01 -7.12252812e+01 1.14874543e+02 -2.05784735e+02 2.63228155e+02 -5.12599003e+02 4.51557712e+02 -4.27822196e+02 4.16379529e+02 -4.12386624e+02 4.26911391e+02 -4.55451722e+02 5.15603525e+02 -6.21695362e+02 7.28767417e+01 -1.30555281e+02 8.14130017e+01 -5.29778090e+01 1.05118930e+01 2.78138223e+01 -8.47999911e+01 1.38330200e+02 -2.42834678e+02 2.69722273e+02 -7.80975534e+02 5.97931073e+02 -5.63607658e+02 5.23362993e+02 -4.99952577e+02 4.86780444e+02 -4.88405162e+02 5.06768285e+02 -5.49862716e+02 6.12957640e+02 -7.61585758e+02 7.45220077e+01 -2.68157227e+02 1.91824059e+02 -1.58746646e+02 1.08315362e+02 -6.64923687e+01 1.43166352e+01 3.45326157e+01 -1.04271248e+02 1.60971980e+02 -2.84914779e+02 1.81871340e+02 -1.23818077e+03 7.45652875e+02 -7.09615140e+02 6.27780651e+02 -5.82522225e+02 5.40792956e+02 -5.19442103e+02 5.09950545e+02 -5.22911966e+02 5.51952458e+02 -6.18196941e+02 6.76198776e+02 -8.78987056e+02 -1.07788393e+02 -5.21822058e+02 3.69270155e+02 -3.30496959e+02 2.52349463e+02 -1.96407263e+02 1.29813401e+02 -7.27551765e+01 5.23471678e+00 5.40027567e+01 -1.40379935e+02 1.79407887e+02 -3.16954520e+02 -2.06762800e+02 -2.08961316e+03 7.27805441e+02 -7.34731770e+02 5.81023372e+02 -5.14291547e+02 4.35390700e+02 -3.91459438e+02 3.54532382e+02 -3.45373462e+02 3.51235232e+02 -3.91091555e+02 4.43865494e+02 -5.49949238e+02 5.59008556e+02 -7.89160500e+02 -9.66718995e+02 -1.02229409e+03 6.60907000e+02 -6.21745676e+02 4.72081886e+02 -3.80992013e+02 2.67227283e+02 -1.79411581e+02 8.34003976e+01 -3.72634381e+00 -8.60425587e+01 1.50676774e+02 -2.52874677e+02 2.06678162e+02 -2.99541972e+02 -1.52201308e+03 -3.91987441e+03 1.31956812e-01 -2.10731797e+02 -8.51952916e+01 1.56801941e+02 -2.88033218e+02 3.48216354e+02 -4.16015812e+02 4.40013344e+02 -4.52262747e+02 4.23908169e+02 -3.74801433e+02 2.71071742e+02 -1.68589205e+02 -2.37181842e+00 -2.13215530e+02 2.24351305e+02 -4.14528536e+03 -2.05808942e+03 1.10812882e+03 -1.09916360e+03 7.66321566e+02 -5.90036371e+02 3.49403118e+02 -1.76485648e+02 -1.29714512e+01 1.62616241e+02 -3.13114491e+02 4.28650702e+02 -5.48853019e+02 5.96027037e+02 -6.92493837e+02 3.49067704e+02 -1.25252721e+02 -5.69039811e+03 -8.56571038e+03 -3.06887665e+03 2.13044375e+03 -2.74911846e+03 2.75488178e+03 -2.96359505e+03 3.00905461e+03 -3.11044452e+03 3.12902168e+03 -3.15214453e+03 3.11515403e+03 -3.05614511e+03 2.93109866e+03 -2.77376039e+03 2.52042043e+03 -2.32033172e+03 2.07508213e+03 -3.16930993e+03 4.80651824e+03 -1.50009599e+04 -4.26765598e+03 1.61388366e+03 -1.75966366e+03 9.46458816e+02 -5.59849134e+02 -1.73114672e+01 4.17790613e+02 -8.70820340e+02 1.22559417e+03 -1.57982677e+03 1.86131898e+03 -2.11996642e+03 2.29463842e+03 -2.45095684e+03 2.40087972e+03 -2.39141518e+03 1.09036161e+03 5.36797665e+02 -1.87814068e+04]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.11 0.11 [[-4.38734192e+02 -4.08597311e+02 2.55041032e+02 -5.02380587e+02 7.15443696e+02 -6.57720042e+02 -1.95179499e+02 1.70269589e+02 -3.82884709e+02 4.21339058e+02 -5.97891776e+02 6.51837023e+02 -6.39761561e+02 7.63333651e+02 -8.29324803e+02 -1.04118023e+02 8.32107392e+01 -1.94669365e+02 2.50703234e+02 -4.27520662e+02 5.27529362e+02 -7.89005791e+02 7.62863186e+02 -7.36405226e+02 7.55759186e+02 -7.84696109e+02 8.93527268e+02 -1.03052415e+03 -6.60424174e+01 -1.01990109e+01 -8.70247041e+01 1.26004503e+02 -2.30654595e+02 3.12350432e+02 -4.98081385e+02 6.14686412e+02 -1.09612978e+03 9.93085397e+02 -9.41490780e+02 9.05413406e+02 -8.93972569e+02 9.08092653e+02 -9.60682826e+02 1.06789477e+03 -1.27287215e+03 -1.15815271e+02 -1.61687233e+02 1.72088340e+01 -5.66495880e+00 -9.55456390e+01 1.54382405e+02 -2.75659762e+02 3.75867381e+02 -5.90154537e+02 6.49042031e+02 -1.53360381e+03 1.32383192e+03 -1.22564421e+03 1.14297992e+03 -1.09164614e+03 1.04522371e+03 -1.04632729e+03 1.06349842e+03 -1.14520217e+03 1.25399678e+03 -1.54476215e+03 -4.45733157e+02 -4.80176623e+02 1.54166255e+02 -2.20604381e+02 6.64579393e+01 -2.81437788e+01 -9.57630893e+01 1.74188913e+02 -3.24269006e+02 4.31177084e+02 -6.87497036e+02 4.92515391e+02 -2.04112865e+03 1.68513477e+03 -1.47086985e+03 1.36349110e+03 -1.25828711e+03 1.15246927e+03 -1.11339617e+03 1.06672924e+03 -1.09394650e+03 1.12771252e+03 -1.25693763e+03 1.35109763e+03 -1.75242019e+03 -1.73798986e+03 -1.26514316e+03 3.41863021e+02 -6.64263075e+02 3.30255361e+02 -3.36817511e+02 1.43568653e+02 -7.41134105e+01 -8.82172258e+01 1.88008481e+02 -3.78818820e+02 4.53301795e+02 -7.43637092e+02 -2.81476075e+02 -2.25438942e+03 1.83408955e+03 -1.23080698e+03 1.23072993e+03 -1.02300348e+03 8.64700843e+02 -7.91898386e+02 6.81774456e+02 -6.81490341e+02 6.60857082e+02 -7.50075239e+02 8.28693470e+02 -1.04272968e+03 1.03595843e+03 -1.49887987e+03 -6.31739258e+03 -3.40254628e+03 5.01255536e+02 -1.68779694e+03 7.95148655e+02 -9.47063589e+02 5.30184599e+02 -4.61069133e+02 1.90194622e+02 -6.97922560e+01 -1.54824425e+02 2.72085825e+02 -5.10739630e+02 4.24435985e+02 -6.39532674e+02 -2.95914160e+03 -7.21131598e+02 1.13983797e+03 9.68519243e+02 -2.64054520e+02 7.66693849e+02 -8.93116337e+02 9.89095676e+02 -1.15640591e+03 1.14295984e+03 -1.20570761e+03 1.10281328e+03 -1.03270768e+03 7.95952542e+02 -6.13122633e+02 2.54585311e+02 -7.08247887e+02 7.09671671e+02 -2.20609166e+04 -9.62002792e+03 5.41157616e+01 -4.19101934e+03 1.53560767e+03 -2.19848491e+03 1.11670554e+03 -1.04972070e+03 4.36567512e+02 -2.18274094e+02 -2.06306220e+02 4.20320368e+02 -7.45103130e+02 8.49761810e+02 -1.11168505e+03 4.56152262e+02 -6.72095184e+01 -1.14750710e+04 7.81389631e+03 -1.61020204e+03 9.71646390e+03 -5.90893784e+03 7.51265239e+03 -7.18485660e+03 7.31617352e+03 -7.47176102e+03 7.34523689e+03 -7.41690723e+03 7.19869404e+03 -7.09304434e+03 6.73740934e+03 -6.42310142e+03 5.84108570e+03 -5.43855145e+03 4.89419915e+03 -7.08727237e+03 1.02971117e+04 -7.56420372e+04 -2.86029104e+04 -3.72598656e+03 -1.05685617e+04 2.22038492e+03 -4.70849019e+03 1.63547075e+03 -1.64336854e+03 3.65332144e+01 4.83654069e+02 -1.53921867e+03 2.08965107e+03 -2.82964589e+03 3.22433466e+03 -3.70668265e+03 3.67605390e+03 -3.80038790e+03 1.28999539e+03 1.84369606e+03 -3.81536281e+04]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.09 0.1 [[-3.32474006e+01 -3.43102957e+01 1.49843938e+01 -4.44866987e+01 6.62323065e+01 -5.57471527e+01 -1.31135059e+01 1.13537365e+01 -3.31716458e+01 3.37287741e+01 -5.38503634e+01 6.00614592e+01 -5.80290924e+01 7.14166594e+01 -7.43205356e+01 -3.81242113e+00 2.99269935e+00 -1.44053530e+01 1.98860013e+01 -3.79987727e+01 4.65959645e+01 -7.15325419e+01 7.20572795e+01 -6.79526004e+01 7.10511368e+01 -7.29635949e+01 8.48799421e+01 -9.53974900e+01 1.49687192e+00 -6.77467823e+00 -4.02529468e+00 7.67305512e+00 -1.86852318e+01 2.68809229e+01 -4.56541639e+01 5.86974419e+01 -9.85593381e+01 9.74091784e+01 -8.80807144e+01 8.75235775e+01 -8.44589327e+01 8.74400857e+01 -9.13917580e+01 1.03436402e+02 -1.20402207e+02 8.05328833e-01 -2.31031262e+01 5.39757622e+00 -5.75216889e+00 -6.12865477e+00 1.14265935e+01 -2.42508297e+01 3.47700132e+01 -5.61826819e+01 6.94653628e+01 -1.32657317e+02 1.37774696e+02 -1.15772485e+02 1.15952049e+02 -1.05459801e+02 1.04943188e+02 -1.02259470e+02 1.05974927e+02 -1.12228442e+02 1.25251586e+02 -1.49402522e+02 -2.08265441e+01 -5.74769854e+01 1.50314308e+01 -2.80865191e+01 7.29169949e+00 -6.75784202e+00 -8.00078188e+00 1.49771137e+01 -3.08752002e+01 4.33405503e+01 -6.89885619e+01 7.26468199e+01 -1.56638593e+02 1.96308170e+02 -1.39039935e+02 1.52675884e+02 -1.26165650e+02 1.27431720e+02 -1.15823094e+02 1.16525999e+02 -1.15003898e+02 1.20760217e+02 -1.30537919e+02 1.44597721e+02 -1.77626412e+02 -1.18170488e+02 -1.41641017e+02 1.96248425e+01 -7.49050758e+01 2.45684054e+01 -3.81817018e+01 1.14253393e+01 -1.01163955e+01 -9.15887267e+00 1.79534560e+01 -3.83074453e+01 5.11665576e+01 -8.16344003e+01 4.71453626e+01 -9.95795353e+01 2.77398004e+02 -1.10968174e+02 1.83467623e+02 -1.14249083e+02 1.33867373e+02 -1.03850353e+02 1.09009380e+02 -9.77718544e+01 1.02330452e+02 -1.04292277e+02 1.14661565e+02 -1.30731988e+02 1.42809564e+02 -1.83475695e+02 -4.80923047e+02 -3.70373363e+02 -1.06975520e+01 -1.86190193e+02 4.24809922e+01 -1.03148623e+02 3.78551585e+01 -5.28554952e+01 1.67894400e+01 -1.40186423e+01 -1.18901973e+01 2.25649585e+01 -4.86883139e+01 5.61124832e+01 -8.73672785e+01 -7.19394007e+01 2.91567196e+02 3.98924230e+02 1.28604254e+02 1.62484588e+02 3.67776570e+01 5.98000653e+01 1.88370587e+01 1.47916397e+01 1.56604552e+01 -3.34527379e-01 6.71087545e+00 7.63553150e+00 -1.90571613e+01 3.93513356e+01 -6.66364206e+01 6.17728717e+01 -8.62364736e+01 -1.75877314e+03 -1.03682641e+03 -2.01375253e+02 -4.71836374e+02 2.83830436e+01 -2.48190802e+02 6.23835704e+01 -1.32169702e+02 4.25647832e+01 -5.74997721e+01 6.48421063e+00 -7.60106500e-01 -3.49775276e+01 4.63467619e+01 -7.82858903e+01 6.07018412e+01 -6.82376133e+01 -4.83836053e+02 1.87845856e+03 6.67439118e+02 1.09611979e+03 -3.25670259e+01 6.57371608e+02 -2.81356434e+02 5.08627914e+02 -3.64268447e+02 4.46039251e+02 -3.81926020e+02 4.03468212e+02 -3.64271595e+02 3.54097867e+02 -3.16394663e+02 2.81332089e+02 -2.40400038e+02 1.95471832e+02 -2.77411869e+02 4.01381233e+02 -6.16904921e+03 -3.07624637e+03 -1.03614398e+03 -1.24736170e+03 -1.75646931e+02 -5.83520046e+02 1.57184917e+01 -2.73471459e+02 1.84412078e+01 -9.47458134e+01 -3.56083074e+01 2.60505680e+01 -1.02116112e+02 1.13925021e+02 -1.63812818e+02 1.69902023e+02 -2.01288323e+02 9.54548745e+01 2.51103550e+01 -1.81080058e+03]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.06 0.13 [[-6.36997635e+01 -1.12659141e+02 -1.85706395e+02 -3.72616224e+02 4.68639482e+02 -1.92927581e+02 -1.69231814e+01 3.87156757e+00 -1.38394238e+02 -4.34062355e+01 -5.01041397e+02 5.07514385e+02 -4.52819706e+02 5.09849716e+02 -3.44648288e+02 2.16028243e+01 -2.25926494e+01 -4.94488716e+01 5.85446643e+01 -1.58877276e+02 4.60001332e+01 -6.96653849e+02 6.80626741e+02 -6.17569833e+02 6.10654258e+02 -5.77817039e+02 6.11387530e+02 -5.12528665e+02 4.44452385e+01 -7.97880947e+01 1.96066225e+00 7.34160405e+00 -7.50813180e+01 9.92258443e+01 -1.92541443e+02 1.14962561e+02 -9.77176746e+02 9.82191274e+02 -8.66253684e+02 8.34983400e+02 -7.71799657e+02 7.56295272e+02 -7.33324753e+02 7.52904268e+02 -7.11684689e+02 2.94890469e+01 -1.92997829e+02 4.71568315e+01 -7.36201961e+01 -1.31823498e+01 2.78300310e+01 -1.03509024e+02 1.38131146e+02 -2.39144695e+02 1.61174423e+02 -1.30003134e+03 1.45331613e+03 -1.20021064e+03 1.18041158e+03 -1.04673224e+03 1.00804315e+03 -9.39609837e+02 9.20167581e+02 -9.08294314e+02 9.20693906e+02 -9.62543252e+02 -1.32724983e+02 -4.52523212e+02 7.62492831e+01 -2.28889955e+02 5.33492418e+01 -8.29453957e+01 -2.24843482e+01 4.01345002e+01 -1.30342790e+02 1.68660378e+02 -2.89910380e+02 1.48104521e+02 -1.41114843e+03 2.15931537e+03 -1.52406089e+03 1.64043101e+03 -1.34272364e+03 1.32280682e+03 -1.16941371e+03 1.13271995e+03 -1.06431155e+03 1.05113558e+03 -1.06532255e+03 1.07757857e+03 -1.29472026e+03 -8.38782120e+02 -1.12956463e+03 6.62273740e+00 -5.81063733e+02 1.20418747e+02 -2.96808504e+02 7.50209535e+01 -1.12398358e+02 -1.98144393e+01 3.58658157e+01 -1.45125014e+02 1.71171947e+02 -3.27170890e+02 -3.05899424e+01 -3.23422327e+02 3.21766716e+03 -1.40906401e+03 2.11971180e+03 -1.38052916e+03 1.53702135e+03 -1.19441390e+03 1.20190931e+03 -1.04084311e+03 1.02995785e+03 -9.84456628e+02 1.00307546e+03 -1.08818548e+03 1.11261624e+03 -1.76156971e+03 -3.48412887e+03 -3.06022772e+03 -5.14728900e+02 -1.46592423e+03 9.73917778e+01 -7.61420699e+02 1.70508455e+02 -3.83801502e+02 9.31850435e+01 -1.43382092e+02 -2.38371571e+01 2.56987164e+01 -1.52856646e+02 1.22291576e+02 -3.32687617e+02 -6.38885325e+02 5.39489545e+03 4.98578205e+03 6.65173463e+02 2.25916169e+03 -1.85242908e+02 1.09189059e+03 -2.44585906e+02 5.23836823e+02 -1.62181840e+02 2.57412788e+02 -1.32826567e+02 2.07633683e+02 -2.50931627e+02 3.66651064e+02 -6.42364561e+02 7.08589486e+02 -2.47458625e+03 -1.29492792e+04 -8.89120678e+03 -2.85564191e+03 -3.83735192e+03 -4.69720276e+02 -1.82256852e+03 6.58257040e+01 -8.61719599e+02 6.82055354e+01 -3.24392540e+02 -6.85258204e+01 -3.95387910e+00 -2.01310010e+02 1.51444186e+02 -2.74965701e+02 6.28349849e+01 -3.54368399e+02 -2.29364062e+03 2.72432406e+04 8.99740841e+03 9.69318760e+03 1.06651982e+03 5.34397740e+03 -1.68578307e+03 4.06521945e+03 -2.71449126e+03 3.61887459e+03 -3.05672390e+03 3.34624761e+03 -3.02427130e+03 2.98908243e+03 -2.68093215e+03 2.37540654e+03 -1.99606403e+03 1.21791083e+03 -1.03347034e+03 -3.67319960e+03 -4.62621140e+04 -2.72155775e+04 -1.20901441e+04 -1.05037666e+04 -3.46104918e+03 -4.30292301e+03 -1.18633811e+03 -1.56770315e+03 -8.04592404e+02 -1.36537338e+02 -9.80730984e+02 6.88264578e+02 -1.23194916e+03 1.11118742e+03 -1.33243424e+03 1.11924133e+03 -1.15063116e+03 4.07644667e+02 -8.09675468e+02 -6.36842923e+03]]
<ipython-input-14-2bfc2a07c69d>:10: RuntimeWarning: overflow encountered in exp out = 1/ (1+np.exp(h)) <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: divide by zero encountered in log c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m <ipython-input-14-2bfc2a07c69d>:24: RuntimeWarning: invalid value encountered in multiply c = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))/m
nan nan 0.09 0.1 [[-6.66069081e+02 -6.90408458e+02 3.06537242e+02 -8.91370387e+02 1.32523080e+03 -1.11886586e+03 -2.64129465e+02 2.27933029e+02 -6.66253916e+02 6.78407064e+02 -1.07644005e+03 1.19943097e+03 -1.16043991e+03 1.42691610e+03 -1.48946699e+03 -7.73166364e+01 5.83189635e+01 -2.88049578e+02 3.96975726e+02 -7.61935221e+02 9.32876892e+02 -1.42793084e+03 1.43686327e+03 -1.35559072e+03 1.41670356e+03 -1.45707002e+03 1.69296060e+03 -1.91024842e+03 3.00246195e+01 -1.39756053e+02 -7.74596824e+01 1.49919106e+02 -3.71719326e+02 5.34426309e+02 -9.14851376e+02 1.16943227e+03 -1.96609443e+03 1.94030257e+03 -1.75478366e+03 1.74232397e+03 -1.68315074e+03 1.74049922e+03 -1.82416653e+03 2.05812457e+03 -2.41278032e+03 1.58451413e+01 -4.69472860e+02 1.13504165e+02 -1.23114964e+02 -1.16326708e+02 2.20735064e+02 -4.81129078e+02 6.87888334e+02 -1.12726154e+03 1.37286509e+03 -2.64741636e+03 2.74358477e+03 -2.30440353e+03 2.30573617e+03 -2.09758159e+03 2.08539513e+03 -2.03547494e+03 2.10551864e+03 -2.23935021e+03 2.48327507e+03 -3.00308217e+03 -4.20577701e+02 -1.15939952e+03 3.10012804e+02 -5.75223134e+02 1.56734182e+02 -1.48795519e+02 -1.50527843e+02 2.86322716e+02 -6.12281921e+02 8.51399709e+02 -1.39064076e+03 1.40881905e+03 -3.13232344e+03 3.91089940e+03 -2.76594725e+03 3.03339856e+03 -2.50567245e+03 2.52763252e+03 -2.29877173e+03 2.31001841e+03 -2.28545180e+03 2.39175066e+03 -2.60733380e+03 2.84686976e+03 -3.60083274e+03 -2.38138655e+03 -2.84248973e+03 4.03226714e+02 -1.51787553e+03 5.08714322e+02 -7.85604630e+02 2.46930903e+02 -2.23618215e+02 -1.67902549e+02 3.37833153e+02 -7.60508564e+02 9.90709751e+02 -1.66684782e+03 8.30302795e+02 -2.01181960e+03 5.53620795e+03 -2.20410867e+03 3.64198410e+03 -2.25952165e+03 2.64517797e+03 -2.04929463e+03 2.14821963e+03 -1.92909689e+03 2.01471642e+03 -2.06604445e+03 2.25312011e+03 -2.62198303e+03 2.75726898e+03 -3.81309641e+03 -9.67911522e+03 -7.40354334e+03 -2.09607957e+02 -3.74820034e+03 8.73257830e+02 -2.09723070e+03 7.86845796e+02 -1.09170573e+03 3.63977526e+02 -3.13539342e+02 -2.16070385e+02 4.16208220e+02 -9.73667997e+02 1.05248149e+03 -1.84670773e+03 -1.72709898e+03 5.74734139e+03 7.99089201e+03 2.57459316e+03 3.21915245e+03 7.67293446e+02 1.14920841e+03 4.18203727e+02 2.46332806e+02 3.55685748e+02 -5.51543691e+01 1.72201707e+02 1.03574894e+02 -3.58013229e+02 7.13854292e+02 -1.37831733e+03 1.00656892e+03 -2.13040677e+03 -3.53523382e+04 -2.06795201e+04 -4.05472970e+03 -9.45537736e+03 5.92020704e+02 -5.01100517e+03 1.29079759e+03 -2.69790210e+03 8.98139317e+02 -1.20350721e+03 1.71936126e+02 -6.58726210e+01 -6.71121405e+02 8.65247329e+02 -1.58847831e+03 1.05594206e+03 -1.64251570e+03 -1.04194236e+04 3.73088415e+04 1.34471445e+04 2.18715088e+04 -6.65261532e+02 1.31743691e+04 -5.68714969e+03 1.02296325e+04 -7.35979411e+03 8.98595073e+03 -7.71356359e+03 8.13283630e+03 -7.35744922e+03 7.13494643e+03 -6.40619580e+03 5.64489999e+03 -4.95253960e+03 3.75187629e+03 -6.10140338e+03 6.91715164e+03 -1.23856315e+05 -6.13124650e+04 -2.08506513e+04 -2.49220757e+04 -3.51102555e+03 -1.17235449e+04 3.68519157e+02 -5.54983643e+03 4.40138451e+02 -1.97960150e+03 -6.40965313e+02 4.40816585e+02 -1.98131999e+03 2.19991656e+03 -3.24454288e+03 3.28233023e+03 -4.11945187e+03 1.53119645e+03 -2.54344348e+02 -3.81452468e+04]]